>>Mhm Yes. Hello. Welcome back to the cubes coverage of red hat summit 2021 Virtual. I'm john for your host of the cube we're here with Roberto Pellegrini, head of architecture and digital ai services from stam he's remote promoting in from Milan Italy Roberto. Great to see you. Thanks for joining us on the cube. >>Great to see you too john. Thanks for having me here. >>Love the virtual events. We can bring people in from all around the world. I love the virtual. I mean it's one of the trade us for not being in person as we can still get you in. Thanks for coming on. Before we get started. I want to dig into the digital architecture of what you guys are doing. Very compelling in a hybrid cloud. It's got you got all the things going on which I like. But before we start, can you provide a short overview of Snam who are your customers? What is your company's focus and what's your role there? >>Sure. So his name is one of the worst leading energy infrastructure operators. And we basically beat the energy infrastructures and offering the way the services our mission is to guide the evolution of the energy sector and leave the energy transition to a low carbon future. And as you can see in our last investment plan, we declared our net zero-carbon objective to bridge by 2040. This is why we basically are investing a lot in uh, technology in renovating our technology stack in order to provide our business line with the most innovative sustainable energy network, thanks to which we are already guaranteeing stable supplies to europe of natural gas. >>Love your title. Love the fact you've got the ai piece in there. Um, what about specifically is your role? What do you oversee? >>I'm responsible for architecture, digital and artificial intelligence services. That basically means that uh, with with my team and my extended team of the digital technology department are designing the entire technology stack for islam. And I'm specifically focusing more on developing intelligent than usable services for our business lines >>awesome. You guys were there, it's nam have transformed a lot the stack. That's cool. When you get into that, you redesign your applications map. Right? So it's really edge to cloud now. Edge up to the cloud. What were the business drivers and the objectives to reach that goal? Because that's really a great use case. I mean you got the edge to deal with intelligent, you got industrial, global business drivers and objectives. >>Yeah. Our main business drivers has always been to to increase the effectiveness of our processes and business lines so bear support the decision of our internal line of business. And we soon discover that we need a more data in order to do that. And we structure very extensive of your program. But those data provide information about internal states of our assets because they're coming from the census and we thought what about the environment in which our assets are located. So uh following up on that we integrated data coming from remote sensing technologies. So think about drones and satellites. Major data and we soon discover that we needed to renew and extend our technology stack from edge to cloud as you said. And to be the scalable that the platform in order to process this new level of uh data this way we think we will be able to enter the new volume of data that we predict. It will be 100 times what we currently manage and efficiently use AI and machine learning to the riding side from this new scale and complexity. So we're talking about big data >>repairs. I gotta ask you could you take a minute to describe your transformation journey you guys went through and how red hat helped you guys execute the digital transformation? >>Yeah we basically started working in 2018. We read up to set up our 12 grade in a snap. We basically needed to decide what to scale what you lift and shift what we factor in order to move our application to a modern architectural stack. And right up to us with this uh we use open ship for our container orchestration platform and from this we're developing our new application act. Then in 2019 we decided to accelerate the moving of our application workloads. We started moving 10-20 of our work clothes on open shift. And since then most of our new software project Islam, a club native and developed on open shifts. We're still in the process of leveraging modern architecture. So microservices based and using our continual construction platform and other software as a service platform in order to complete the modernization of our application that And we are targeting 2023 2024 to complete the entire process but as you know, is an ever changing landscape. So you basically never complete such a task in some way. >>Do you see red hat technology helping stem in its ecosystem for energy efficiency and aiming for low carbon emissions? >>I think that open ship provide provide us with the right level of flexibility and agility to move at the speed of our new businesses. That's one way to look at the quest and the other one. Uh huh. I think it would be in terms of energy efficiency and the carbon footprint that our application workloads generate. And I think that uh in that in that respect it could happen in the mid to long term, probably so it will be in proportion to the workloads. We will be able to re factor as purely reactive so as non blocking apps. This probably in fact for the same business service could improve the effective resource consumption, so indirectly saving energy and CO two, >>you know, I love this conversation and I know you're in Italy and wish we could be in person but I'm glad to get you on because you guys are kind of an example of the main theme at the conference this year, which is an edge, you know, intelligent edge and IOT, but you know, I O T has been around for a while and we've talked about it before, but now with the cloud and connecting to the cloud, that's a huge topic here at red hat summit 2021 you guys are well versed on the call O. T. Technology operational technologies and what's interesting is kubernetes and containerized orchestration all help operators, operations people. So you have this ot tight integration where the operational technology, old school technology people and the stack and the people in the disciplines are meeting the old I. T. And creating a new thing. So I have to ask you, what are these, what's that world like what are some of the use cases that you're working on and you're planning to deploy? >>Yeah. Yeah. Yeah exactly. It's exactly like that name has a long ot history as you said. So right now we have a complex brownfield uh, situation for our edges and gateways on the field. There are various technical components that resigns on the field. You must consider that Italian network, the Italian Transportation Network has more than 34,000 km of pipeline and differently sides of plants across the country. So we have several already several use cases currently running on our data centers that could benefit we think from distributed processing at the edge. Think about for example physical security. So just to give you an example privacy preserving local video processing for anomaly detection done at the edge, it's much more effective in our opinion. Poor hierarchical processing for data intensive task that involves field data so that you can process the data coming from the field at different level and take to the central data center only was needed. And we're also working on the usual problem there is with with the Widow Tea with the operation of the countries that is standardization. So we have many exogenous components and communication protocol there and you know without a proper rieti stack gathering and normalizing the data for a higher level process could become cumbersome. So security is also is also a relevant topic because it is usually preserve and the physical and natural layer and we we think that we can introduce variety pre main improvements about this. We're expanding the level of cybersec to the food technologists act, bringing modern internet security standard to the edge. We're pushing continue realization to the edge, being able to orchestrate our work clothes from data centers to the cloud. And we think that we this will provide us with a high level of flexibility and a better exploitation of the geographical distribution of our data. And last but not least we're standardizing our gateways and edges and this will help us streamline the message of the transfer conversion and normalization uh of the data we will receive from the field >>awesome. I gotta, I gotta ask for such a great job on the edge. I think that's a great vision. Uh building insecurity, it's important having that edge intelligent is really well done. Congratulations. Love the vision. I gotta ask you, what's your future plans for um Snam technology journey as a whole. What's your vision? What's your next step? >>So well, what we would like to focus on uh in the coming years is how to best leverage the average cloud environment we currently set up. So right now we have an average cloud environment with the data center and one cloud tenant and having our poor clothes running on open ship would make it easier for us to leverage the offering of different club providers and of course to best exploit what we currently have on our tenants. Second one is find the best way to leverage IOT. So as I said before, our focus in the coming years will be to complete our IOT foundation, rolling out our edges are gateways and put our new unified opposition system to work. And this will provide the computational backbone of our intelligent investments. And finally, uh and this is a less objective that is will be built on top of the other two. We must find different ways and export different ways to leverage data and artificial intelligence. So we need to exploit our data uh in order to generate insight for our business lines. Need to the scale of our new data streams, artificial intelligence machine learning, we think will be ubiquitous in our applications right now. We're already using it but not at the scale uh that the new data streams uh well we'll need and most of the algorithms are working on data that are apart from legacy system and scare the system. So they are specifically created for each project. We're about to begin an exciting data journey where everything will reside on a unified data platform and our data scientists, our data analyst in the business lines will be able to derive value from them >>awesome. You know, you guys are great customer use case. I love the real operational impact. I talked with a lot of other practitioners and end user enterprises and I get the same question and I got the statement. They say actually security needs to be built in, but the challenges and where they want to, what they want to do. And I want to get your thoughts on this. If you don't mind commenting, they all say, I want to run cloud native applications, cloud native applications from my data center to the cloud and then out to the edge and with this as a distributed platform, one operation set, whether it's O T I T I want to make that, that's my endgame. In the short term, I get there fast. So I gotta ask you for those people that want that is open shift, a good solution for that. In your opinion, >>we, we of course think it is uh, it is part of our IOT foundation, uh, is not the only technology components, but is one of the, one of the most relevant and it is absolutely happiness in uh, enabling the possibility of orchestrating more clothes uh, from the cloud to the edge. And we will be able to give you more information about that as soon as uh, we will release the first Distribue work clothes within 2021. So I'll be happy to to answer any any questions from our peers or or other colleagues from other industries. >>You guys have thousands and thousands of sites. This is the classic industrial edge implementation, closely monitoring just monitoring the pipes. I mean, you gotta monitoring the system just physically. I mean, this is like a, just a physical thing. So now as you have technology, you guys have to monitor and get that early detection of any gas leaks, this is critical to your business. Um how is that changing? How is that environment changing with technology is more automated? What's your vision? How are you guys looking at that? >>Well, we, we surely are trying to move along to two main drivers. The first is um, unification and standardization of how we monitor all these distributed technology stock. This is very important because even for the simplest use case, you're now dealing with distributed application and this is a entirely different game to what we are used to basically. And and um uh the the other rather the other relevant thing is how can we get the best from um the machines we put on the field. So in other in other in other terms, how can we standardize how we connect to the machines we have on the field and how much intelligence we need to put there and how to test it. And in order to do that, we're thinking about um building a digital twin of our assets that will enable us to being able to test and to end before getting to the real thing on field. How will it work? What are the security vulnerabilities, potential security vulnerability and other aspects uh of the technology infrastructure and the data infrastructure? And we think this is very important because in some way uh in order to provide the acceleration and the scale that we uh are going to provide uh to, to our company, we need to be sure well in advance that what we designed will work in practice without getting to the field. We would like to get into the field where everything is already tested, >>repair too. Great to have you on the cube. Great to see you. Thanks for coming in from Milan Italy. Um cute virtual. It's one of the benefits and hope to see you in person soon at the next event. But great use case, love your environment, love how you're looking at that platform is a distributed platform and bringing that O T T together data center to the cloud to the edge. That's a really relevant use case and architecture. So congratulations. >>Thank you very much, john and I hope to see you too very soon. Alive >>when I'm in Italy, we're gonna come by and do a site visit and uh, see each other coming on. I appreciate it. Thank you. >>Absolutely. >>Okay. Cube coverage for Red Hat Summit 2021. I'm John for your host. Thanks for watching. Mhm. Mhm. Mhm.

>> Narrator: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a cube conversation. >> Everyone welcome to this cube conversation here in the Palo Alto cube studios. I'm John Furrier your host of theCUBE, with a great guest here, Shanthi Vigneshwaran, who is with the Office of Strategic programs in the Center for Drug Evaluation and Research within the US Food and Drug Administration, FDA, is the Informatica Intelligent Disrupter of the Year award. Congratulations, Shanthi welcome to this cube conversation. Thanks for joining me. >> Thank you for having me. >> Congratulations on being the Informatica Intelligent Disrupter of the year award. Tell us more about the organization. I see FDA everyone's probably concerned these days making sure things going faster and faster, more complex, more things are happening. Tell us about your organization and what you work on. >> FDA is huge, our organization is Center for Drug Evaluation research. And its core mission is to promote public health by ensuring the availability of safety and effective drugs. For example any drugs you go and buy it in the pharmacy today, Our administration helps in trying to approve them and make sure it's so in term of quality and integrity of the marketed products in the industry. My office is specifically Office of strategic programs whose mission is to transform the drug regulatory operations with the customer focus through analytics and informatics. They work towards the advancement for the CDERs public health mission. >> What are some of the objectives that you guys have? What are some things you guys have as your core top objectives of the CDER, the drug research group? >> The core objectives is we wanted to make sure that we are promoting a safe use of the marketed drugs. We want to make sure there's the availability of the drugs that are going to the patients are effective. And also the quality of the drugs that are being marketed are able to protect public health. >> What are some of the challenges that you guys have to take in managing the pharmaceutical safety, because I can only imagine certainly now that supply chains, tracing, monitoring, drug efficacy, safety, all these things are happening. What are some of the challenges in doing all this? >> In our office there are challenges in three different areas. One is the drug regulation challenges because as drugs are being more advanced and as there are more increasingly complex products, and there are challenging in the development area of the drugs, we wanted to make sure here we have a regulation that supports any advancement in science and technology. The other thing is also Congress is actually given new authorities and roles for the FDA to act. For example the Drug Quality and Security Act, which means any drug that's they want to track and trace all the drugs that goes to the public is they know who are the distributors, who are the manufacturers. Then you have the 21st Century Cures Act, and also the CARES Act package which was recently assigned, which also has a lot of the OTC drug regulatory modernization. Then there's also the area of globalization because just as disease don't have any borders, Product safety and quality are no longer on one country. It's basically a lot of the drugs that are being manufactured are overseas and as a result we wanted to make sure there are 300 US ports. And we want to make sure the FDA regulated shipments are coming through correctly to proper venues and everything is done correctly. Those are some the challenges we have to deal with. >> So much going on a lot of moving purchase as people say, there's always drug shortages, always demand, knowing that and tracking it. I can only imagine the world you're living in because you got to be innovative, got to be fast, got to be cutting edge, got to get the quality right. Data is super critical. And can you share take a minute to explain some of the data challenges you have to address and how you did that. Because I mean I could almost just my mind's blown just thinking about how you live it every day. Can you just share some of those challenges that you had to address and how did you do? >> Some of the key challenges we actually see is we have roughly 170,000 regulatory submissions per year. There are roughly 88,000 firm registration and product listing that comes to us, and then there are more than 2 million adverse event reports. So with all these data submissions and organization as such as us we need it, we have multiple systems where this data is acquired and each has its own criteria for validating the data. Adding to it are internal and external stakeholders also want certain rules and the way the data is being identified. So we wanted to make sure there is a robust MDM framework to make sure to cleanse and enrich and standardize the data. So that it basically make sure the trust and the availability and the consistent of the data, is being supplied to published to the CDER regulatory data users. >> You guys are dealing with- >> Otherwise like it's almost to give them a 360 degree view of the drug development lifecycle. Through each of the different phases, both pre market which is before the drug hits the market, and then after it hits the market. We still want to make sure the data we receive still supports a regulatory review and decision making process. >> Yeah, and you got to deliver a consumer product to get people at the right time. All these things have to happen, and you can see it clearly the impacts everyday life. I got to ask you that the database question 'cause the database geek inside of me is just going okay. I can only imagine the silos and the different systems and the codes, because data silos is big document. We've been reporting on this on theCUBE for a long time around, making data available automation. All these things have to happen if there's data availability. Can you just take one more minute talk about some of the challenges there because you got to break down the silos at the same time you really can't replace them. >> That's true. What we did was we did leave it more of us I mean, step back like seven years ago, when we did the data management. We had like a lot of silo systems as well. And we wanted to look at we wanted to establish a, we knew we wanted to establish a master data management. So we took a little bit more of a strategic vision. And so what we ended up saying is identifying what are the key areas of the domain that will give us some kind of a relationship. What are the key areas that will give us the 360 degree lifecycle? So that's what we did. We identified the domains. And then we took a step back and said and then we looked at what is the first domain we wanted to tackle. Because we know what are these domains are going to be. And then we were like, okay, let's take a step back and say which is the domain we do it first that will give us the most return on investment, which will make people actually look at it and say, hey, this makes sense. This data is good. So that's what we ended up looking at. We looked at it as at both ends. One is from a end user perspective. Which is the one they get the benefit out of and also from a data silo perspective which is the one data domains that are common, where there's duplication that we can consolidate. >> So that's good. You did the work up front. That's critical knowing what you want to do and get out of it. What were some of the benefits you guys got out of it. From an IT standpoint, how does that translate to the business benefits? And what was achieved? >> I think the benefits we got from the IT standpoint was a lot of the deduplication was not theirs. Which basically means like a lot of the legacy systems and all of the manual data quality work we had to do we automated it. We had bots, we also had other automation process that we actually put into work with Informatica, that actually helped us to make sure it's the cost of it actually went for us considerably. For example it used to take us three days to process submissions. Now it takes us less than 24 hours to do it, for the users to see the data. So it was a little bit more, we saw the, we wanted to look at what are the low hanging fruits where it's labor intensive and how can we improve it. That's how we acted there. >> What are some of the things that you're experiencing? I mean, like, we look back at what it was before, where it is now? Is it more agility, you more responsive to the changes? Was it an aspirin? Was it a complete transformation? Was some pain reduced? Can you share just some color commentary on kind of before the way it was before and then what you're experiencing now? >> So for us, I think before, we didn't know where the for us, I mean, I wouldn't say we didn't know it, when we have the data, we looked at product and it was just product. We looked at manufactured they were all in separate silos. But when we did the MDM domain, we were able to look at the relationship. And it was very interesting to see the relationship because we now are able to say is. for example, if there is a drug shortage during due to hurricane, with the data we have, we can narrow down and say, Hey, this area is going to be affected which means these are the manufacturing facilities in that area , that are going to be not be able to function or impacted by it. We can get to the place where the hurricane tracks we use the National Weather Service data, but it helps us to narrow down some of the challenges and we can able to predict where the next risk is going to be. >> And then before the old model, there was either a blind spot or you were ad hoc, probably right? Probably didn't have that with you. >> Yeah, before you were either blind or you're doing in a more of a reactionary not proactively. Now we are able to do a little bit more proactively. And even with I mean drug shortages and drug supply chain are the biggest benefit we saw with this model. Because, for us the drug supply chain means linking the pre and post market phases that lets us know if there's a trigger and the adverse events, we actually can go back to the pre market side and see where the traceability is who's at that truck. What are all the different things that was going on. >> This is one of the common threats I see in innovation where people look at the business model and data and look at it as a competitive advantage, in this case proactivity on using data to make decisions before things happen, less reactivity. So that increases time. I mean, that would probably you're saying, and you get there faster, if you can see it, understand it, and impact the workflows involved. This is a major part of the data innovation that's going on and you starting to see new kinds of data whereas has come out. So again, starting to see a real new changeover to scaling up this kind of concept almost foundationally. What's your thoughts just as someone who's a practitioner in the industry as you start to get this kind of feelings and seeing the benefits? What's next, what do you see happening because you haven't success. How do you scale it? What how do you guys look at that? >> I think our next is we have the domains and we actually have the practices that we work. We look at it as it's basically data always just changes. So we look at is like what are some of the ways that we can improve the data? How can we take it to the next level. Because now they talk about power. They are also warehouse data lakes. So we want to see is how can we take these domains and get that relationship or get that linkages when there is a bigger set of data that's available for us. What can we use that and it actually we think there are other use cases we wanted to explore and see what is the benefit that we can get a little bit more on the predictability to do like post market surveillance or like to look at like safety signals and other things to see what are the quick things that we can use for the business operations. >> It's really a lot more fun. You're in there using the data. You're seeing the benefits and real. This is what clouds all about the data clouds here. It's scaling. Super fun to talk about and excited. When you see the impacts in real time, not waiting for later. So congratulations. You guys have been selected and you receive recognition from Informatica as the 2020, Intelligent Disrupter of the year. congratulations. What does that mean for your organization? >> I think we were super excited about it. But one thing I can say is when we embarked on this work, like seven years ago, or so, problem was like we were trying to identify and develop new scientific methods to improve the quality of our drugs to get that 360 degree view of the drug development lifecycle. The program today enables FDA CDER to capture all the granular details of data we need for the regulatory data. It helps us to support the informed decisions that we have to make in real time sometimes or and also to make sure when there's an emergency, we are able to respond with a quick look at the data to say like, hey this is what we need to do. It also helps the teams. It recognizes all the hard work. And the hours we put into establishing the program and it helped to build the awareness within FDA and also with the industry of our political master data management is. >> It's a great reward to see the fruits of the labor and good decision making I'm sure it was a lot of hard work. For folks out there watching, who are also kind of grinding away in some cases, some cases moving faster. You guys are epitome of a supply chain that's super critical. And speed is critical. Quality is critical. A lot of days critical. A lot of businesses are starting to feel this as part of an integrated data strategy. And I'm a big proponent. I think you guys have have a great example of this. What advice would you have for other practitioners because you got data scientists, but yet data engineers now who are trying to architect and create scale, and programmability, and automation, and you got the scientists in the the front lines coming together and they all feed into applications. So it's kind of a new things go on. Your advice to folks out there, on how to do this, how to do it right, the learnings, share. >> I think the key thing I, at least for my learning experience was, it's not within one year you're going to accomplish it, It's kind of we have to be very patient. And it's a long road. If you make mistakes, you will have to go back and reassess. Even with us, with all the work we did, we almost went back a couple of the domains because we thought like, hey, there are additional use cases how this can be helpful. There are additional, for example, we went with the supply chain, but then now we go back and look at it and say like, hy, there may be other things that we can use with the supply chain not just with this data, can we expand it? How can we look at the study data or other information so that's what we try to do. It's not like you're done with MDM and that is it. Your domain is complete. It's almost like you look at it and it creates a web and you need to look at each domain and you want to come back to it and see how it is you have to go. But the starting point is you need to establish what are your key domains. That will actually drive your vision for the next four or five years. You can't just do bottom up, it's more of like a top down approach. >> That's great. That's great the insight. And again, it's never done. I mean, it's data is coming. It's not going away. It's going to be integrated. It's going to be shared. You got to scale it up. A lot of hard work. >> Yeah. >> Shanthi thank you so much for the insight. Congratulations on your receiving the Disrupter of the Year Award winner for Informatica. congratulations. Intelligence >> Yeah, thank you very much for having me. Thank you. >> Thank you for sharing, Shanthi Vigneshswaran is here, Office of Strategic programs at the Center for Drug Evaluation and Research with the US FDA. Thanks for joining us, I'm John Furrier for theCUBE. Thanks for watching. (soft music)

(upbeat music) >> From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is theCUBE conversation. >> Hey, welcome back, everybody, Jeff Frick here with theCUBE. We're in our Palo Alto studios today. We're having a CUBE Conversation around a really interesting topic. It's applied AI, applied machine learning. You know, we hear a lot about artificial intelligence and machine learning in kind of the generic sense, but I think really, where we're going to see a lot of the activity is when that's applied to specific solutions and specific applications. And we're really excited to have our next guest. He's applying AI and machine learning in a really interesting and important space. So joining us from San Francisco is Bryton Shang. He's the founder and CEO of Aquabyte. Bryton great to see you. >> Yeah, Jeff. Great to be here. >> I can't believe it's been almost a year since we met at a Kosta Noah event. I looked it up June of last year. Wow, how time flies. But before we get into it, give everyone just kind of the quick overview of what you guys are up to at Aquabyte. >> Aquabyte's a company, we're building software to be able to help fish farmers. It's computer vision and machine learning software based on a camera that takes pictures of a fish in a fish pen, analyzes those images and helps the farmer understand the health of the fish, the weight of the fish, how much to feed and generally better manage their farms. >> It's such a great story. So for those people that haven't seen it, I encourage you to jump on the internet and look up the AWS special that Werner did on Aquabyte last year. It's a really nice piece, really gets into the technology and a lot of the fun part of the story. I really enjoyed it and you know, congratulations to you for getting featured in that AWS piece. But let's go to how did you get here? I mean, you're really interesting guy. You're a multiple company founder coming out of Princeton, in most of your startup role, your startups are all about, Applied Mathematics and Statistics but you've been in everything from finance and trading to looking at cells in the context of Cancer. How did you get to Aquabyte? Was it the technology? And then you found a cool solution? Or did you hear about, you know, an interesting problem and you thought, you know, I have just the trick to help attack that problem. >> Well, so I had studied Operations Research and Financial Engineering at Princeton, which I guess we would call nowadays, like modern day machine learning and data science. So that was something as you mentioned, first I'd apply it to algorithmic trading, and then got on to more general applications of computer vision for example, in cancer detection. The idea to apply machine learning talk to aquaculture, came from a number of different sources. One was from a previous co-founder who had been doing some investigation in the fish farming space, had a business school classmate who owned a fish farm. And also growing up in Ithaca, New York near to Cornell I had a family friend who is a professor of aquaculture. And really just to learn about fish farming and overfishing and the idea that over half the fish we eat nowadays are coming from fish farms and that you could use machine learning and computer vision to make these farms more efficient. That being very interesting and compelling. >> So it's really interesting. One of the things that jumped out from me when I watched the piece with Werner was the amazing efficiency on the feed to protein output in fish farming. I had no idea that it was so high, it's basically approaching one to one really interesting opportunity. And I had no idea to that, as you said over 50% of the world's seafood that's consumed was commercially farmed. So really a giant opportunity and so great space to be in a lot of environmental impacts. So but how did you decide to find an entree? We know where to find an entree for machine learning to make a big impact in this industry. >> So it came from a couple different angles. First, there's been applications of machine learning computer vision and other industries that served as good parallels where we're using cameras to be able to take images and then use computer vision to derive insight from those images. For example, just take aquaculture where you're using cameras to spray weeds to understand crop yield. And so there's good parallels and other industries. aquaculture specifically, I was also looking at what was coming out in the machine learning literature in terms of using cameras to size fish. And so the idea that you could use cameras to size fish was very interesting because then you can use that to figure out growth rates and feeding. And as I developed my idea, it really became clear that you could use computer vision and machine learning to do a wide range of things at the farm and so, it started with this idea about using cameras to size fish and then it became monitoring health and sea lice and parasites and then ultimately, all the aspects of the farm that you would want to manage. >> And correct me for wrong, but do you guys identify individual fish within the population within that big net and then you're basically tracking individuals and then aggregating that to see the health of the whole population. >> That's right, the spot pattern on the fish is unique and we have an algorithm that's able to use that to determine each individual fish via the spot pattern. >> Wow. And then how long once, once you kind of got together with the farmers to really start to say, wow, we can use this application for, as you said, worrying about lice and disease control and oh wow, we can use this application to measure growth. So now we know the health of the environment or wow, now we know the size so we can impact our harvest depending on what our customers are looking for. I assume there's all kinds of ways you can slice and dice the data that comes out of the system into actual information that can be applied in lots of different ways. >> Right So I started the company back in 2017. And if you think about aquaculture, it's actually a hugely international industry 99% outside the US, and within aquaculture, very quickly zeroed in on salmon farming, and specifically salmon farming in Norway. Norway produces about half of the world's farmed salmon and ended up going there for a conference Aqua Nor August of 2017 and whilst there had my idea and a prototype for sizing the fish with a camera, but then also realized in Norway they have recently passed regulations around counting sea lice on the fish so this is parasite that attaches to the fish and is regulated and pretty much every country that grows fish in the ocean and farmers asked me then, okay, if you could use the camera to size fish, can you also count sea lice? And can you also detect the appetite? And then it just turned into this more platform approach where this single camera could do a wide variety of application. >> That's awesome. And I'm just curious to get your take on, the acceptance and really the excitement around, you know, kind of application of machine learning in this computer vision in terms of the digital transformation of commercial fish farming, because once it sounds like once they discovered the power of this thing, they very quickly saw lots of different applications, and I assume continue to see kind of new applications to apply this to transform their business. >> Right, I would say fish farming itself is already fairly highly mechanized. So you're dealing with fairly rough conditions in the ocean. And a lot of the equipment there is already mechanized. So you have automatic feeders, you have feeding systems. That said, there isn't too much computer vision machine learning in the industry. Today, a lot of that is fairly new to the farmers. That said they were open to trying out the technology, especially when it helps save labor at the farm. And it's something that they have familiarity with, with some of the applications for example, with Tesla with their autopilot and other examples that you could point to in common day use. >> That's interesting that you brought up Tesla, I was going to say that the Tesla had an autonomous driving day presentation. I don't know, it's probably been a year or so now but really long in-depth presentations by some of his key technical people around the microprocessor and AI and machine learning and a whole thing about computer vision. And, you know, there's this great debate about, can you can you have an autonomous car without Lidar and I love the great quote from that thing was you "Lions don't have Lidar "and they chase down gazelles all day long." So, we can do a lot with our vision. I'm curious, some of the specific challenges within working in your environment within working in water and working with all kinds of crazy light conditions. It's funny on that Tesla, they talked about really some of the more challenging environments being like a tunnel, inside of a tunnel with wet pavement. So, kind of reflections and these kind of metric conditions that make it much harder. What are some of the special challenges you guys had to overcome? And how much, is it really the technology? Or is it really being done in the software and the algorithms and the analyzing or is it basically a bunch of pixel dots? >> Right. The basic technology is based on similar, it's a serial camera that takes images of the fish. Now, a lot of the special challenges we deal with relate to the underwater domain. So underwater, you're dealing with a rough environment, there could be particles in the water, specularity some reflections underwater, you're dealing with practical challenges such as algae, but even the behavior of the fish, are they swimming by the camera? Or do you want to position your camera in the pen. Also, water itself has interesting optical properties. So the deeper you go, it affects the wavelength that's hitting the camera. And also you have specialized optics where the focal length and other aspects of the optics are affected underwater. And so a lot of the specific expertise we've developed is understanding how to sense properly underwater. Some of that is handled by the mechanical design. A lot of it is also handled by the software, where on the camera we have GPUs that are processing the images and using deep learning computer vision algorithms to identify fish parts and sea lice and other aspects of the fish. >> It's crazy, and how many fish are in one you know, individuals are in one of these nets. >> So single pen can have as much as 100,000. Where actually in one pen, which is I think it's the largest salmon farm in Norway based on an oil rig called the ocean farm where they have 2 million fish in a single pen. >> 2 million fish, and you're in that one. >> Right, yes. >> And you've identified all 2 million fish or do you work on some sampling? Or how do you make sure every fish eventually swims by the camera? Or does the camera move around inside that population? That's an amazing amount of fish. >> So I think we'll eventually get to the point where we can identify every single fish in the pen and use that to track individual health and growth. Well we practice what we use the individual recognition algorithm the deal is to de-duplicate fish. So a common question we get asked is okay, what if the same fish swims by the camera twice, and so it's used to de-duplicate fish But I think eventually you'd be able to survey the entire population. >> That's crazy. So where do you guys go next Bryton, again you've brought your analytical brain to a number of problems. Do you see kind of expanding the use within the fish industry and kind of a vertical player? Do you see really a horizontal play in different parts of agriculture and beyond to apply some of the techniques and the IP that you guys have built up so far? >> Well, starting with Norwegian salmon, we want to bring this to other countries around the world for other species. So we've expanded to our second species, which is a rainbow trout. We also are, starting with computer vision are building this very interesting data set which we can use to enable other applications. Eventually, we'll get to the point where that data allows us to run fully autonomous fish farms. Right now the limitations of fish farming is that it needs to be close to the shore. So you can have people go to the farms. And once you have fully autonomous fish farms, then you can have fish farms in the open ocean, fish farms on land. And with the world being 70% water, we're only producing about 5% of the protein from the oceans. And so it presents a massive opportunity for us to be able to increase the amount of world's demand for protein. Also given that we're running out of land to grow crops. >> Wow, that's amazing. We're only getting 5% of our food protein out of the ocean at this stage? >> Right, right. >> That is crazy. I thought it would be much higher than that. Well, certainly a really cool opportunity and, a kind of a really awesome little documentary by Werner and the team, definitely go watch it if you haven't seen it. So I just give you the last word as you've been in this industry and really seen kind of the transformative potential of something like computer vision in commercial fishing and who would have even thought that, six or seven years ago? How does that help you kind of think forward, kind of the opportunity really to use these types of applications like computer vision and machine learning to advance something so important, like food creation for our world. >> I think there's definitely a lot of opportunities to be able to use machine learning computer vision, similar technologies to help make these industries a lot more efficient. Also a lot more environmentally sustainable. I'd say something like this industry, like aquaculture, it's not so apparent just if you're in the valley, and even in the US just because 99% of it happens outside the US and so to be able to be familiar with the industry to know that it exists and to build applications itself is a bit of a challenge. I would say that is changing. One of the things that actually came out a couple weeks ago was an executive order to actually start kick starting offshore aquaculture in the US. So it is starting in the US. But more generally, I do think there's a massive opportunity to be able to apply machine and computer vision in new industries that previously haven't been addressed. >> Yeah, that's great. And I just love how you got kind of a single source of data, but really the information that you can apply and the applications you can apply are actually quite broad. It's a super use case. Well, Bryton, thanks for spending a few minutes. I've really enjoyed the story. Congratulations on your funding rounds and your continued success. >> Thanks, and really appreciate to be on and yeah, hope to continue to help bring the world more sustainable seafood. >> Absolutely. Well, thanks a lot Bryton. So he's Bryton and I'm Jeff. You're watching theCUBE. We'll see you next time, thanks for watching. (upbeat music)

>> Announcer: Live from San Francisco, celebrating 10 years of high tech coverage, it's theCUBE! Covering VMworld 2019. Brought to you by VMware and its ecosystem partners. >> Okay, welcome back everyone live CUBE coverage here in San Francisco at VMworld 2019 I'm Jon Furrier, my co-host this segment, Stu Miniman. 10 years Stu it's been a long run. A lot of CUBE alumnis around, we got two here. Honore LaBourdette Vice President go-to-market Telco Edge Cloud at VMware. And Lakshmi Mandyam, Vice President Product Manager, go-to-market Edge IoT at VMware. Great to see you, thanks for coming back. >> Thank you for having us. >> So, I think IoT's going to be a pretty big deal. 5G, jury's still out on 5G but it's looking good. Look, if Pat Gelsinger said it's going to be great, it's probably going to be great. What's new? Give us the update. >> Well, just a commentary on 5G, when you say you think it's going to be great, there is some skepticism in the marketplace because if you go back and look at all the different generations of cellular technologies, it's the odd numbers that have never been successful and the even numbers that have, from a monetary perspective for the telcos. Interesting thing about 5G is because it's such a system-oriented technology, that we do believe that it's going to enable a lot of the capabilities associated with IoT, right? So there's an interdependency between 5G and IoT and IoT and 5G that I think is going to make 5G more successful than any of its predecessors. >> All of us are nerds that geek out on RF and physics. I mean 5G has a lot of skeptics but they're deploying 5G, it's not like it's a vaporware. There are deployments going on in the United States, certainly outside of the United States. So it is real, it's actually happening. The question is what will be the impact to the network effect and what's it going to enable, which will certainly impact the industrial IoT and IoT markets. >> Well so one of the things that's happening with the deployments of 5G isn't just the innovation associated with the spectrum technology of five generations of mobile technology, right? There is an entire transformation happening with the core infrastructure of the telco network. And there's an interdependency there as well, right? So as the telco's software define the infrastructure on which they run all of their services, that then extends all the way through from the cloud to the core to the edge for all of the radio access and everything associated with 5G. >> And we're also seeing on the IoT side that there's a similar transformation going on, 'cause right now when you look at kind of example manufacturing, right? There's a real siloed infrastructure, siloed use cases and people are not able to scale and especially when you start to see the business impact that IoT's actually going to have, because most of the data that's being generated is actually being generated from the devices at the edge. And there's a viewpoint that a lot of the workloads that are actually being generated for the enterprise are actually going to be executed at the edge and when you take those things into consideration, it's really important to have an infrastructure that scales. And just like we've seen in other areas where a sprawl of infrastructure is really not going to be be effective in terms of delivering business value. That's the same problem that we see here. >> That brings up a good point. You mention systems view. I think this is interesting 'cause I think this business model innovation, as well as the architecture. I mean, you become what you're known for in the old infrastructure. You don't want that legacy to be dictating the new things, you mentioned backhaul. That's a topic that people talk about in the cellular business. You got the radios, you backhaul through a network, go to the core. But now you're getting at something different where if you're going to be backhauling, which implies moving packets around, moving data has become a really big problem or concern because the cost to move data, the physics involved, latency is a requirement. Processing at the edge becomes the new architecture. >> Yeah, I think the old paradigm was around moving data to the compute but the new paradigm is going to be moving compute to the data, especially on the edge and the IoT. And this is where managing that whole compute infrastructure is going to be really, really important. And that's what, you know, the VMware Telco Edge-- >> Well, we're going to ask Pat Gelsinger a question that riffs off what Dave asked years ago. Stu, I don't remember what year it was, 2012 or 2013, Dave Vellante asked Pat Gelsinger, "Is security a do-over?" You know Pat's very opinionated, he's like, "Absolutely a do-over." Really risky, bold take to say at that time, turns out he was right. The question I want to preview with you guys is, is the architecture a do-over? Because if you think about it, there's new capabilities, you mentioned the systems view. Is there an opportunity, not to throw it away, but like, just rethink it, get a second chance at deploying large scale edge, cloud, versus backhauling through the data center, maybe backhaul through the cloud. So, to me it's just kind of feels like a do-over. >> Well, there's very much an opportunity to, I'll say evolve rather than to do it over, right? 'Cause do-over kind of implies everybody's going to throw out everything that they have. But when you think about the beauty of software is that now we can have inherent security in all of the aspects of the software defined network all the way through the edge. So if you happened to hear Pat's keynote this morning, you know, he put up a slide of all the different security vendors across all of the different types of, the different areas of the clouds, the different cloud technologies and basically said that there is an opportunity now for us to do for security basically what we did for compute and networking and storage, by software defining that. And so that's the opportunity for security is to leverage all of what you can do with a software defined approach and have security be intrinsic to everything from the cloud to the core to the edge. And specifically for IoT. If you think about Lakshmi's comment about pushing the compute to the apps, and pushing the compute where the applications are going to be, or the user is going to be, I think there's going to be a greater requirement for security actually at the edge than even what we see in the cloud today. >> Lakshmi, you know, one of the comments we made is if you looked at the keynote this morning, the virtual machine is not the center of the the discussion. There's, you know, VMware, now plays a lot of places where that VM is not at the center. If you can bring us up to speed, when VMware looks at the edge architectures and how they're going to work with enterprises there, you know, what are the solutions that you're going to bring to bare out of the portfolio? >> Yeah so we have a, you know, when you think about IoT and there's all these things that are out there, oftentimes when someone installed it in the factory they didn't even update the factory settings, the threat surface of that is just expansive. And so, what we're doing with the product that I'm going to talk about, Pulse, we actually life cycle manage these devices, software updating, making sure that they're compliant with IT kind of security and other requirements. And so, what we see is the architecture, is we see kind of this managed infrastructure at the device level, that then feeds into kind of the thin edge, and you heard Pat talk about it this morning, right? Pulse and NSX and VeloCloud for the thin edge and that kind of, it's a continuum really. You can't define-- >> It's difficult to do. >> It's a continuum of compute ranging from very small footprint all the way up to our Dell EMC announcement. BMC on Dell EMC, sorry. >> We actually did some original research back when, you know, GE was putting together their industrial internet and one of the biggest stumbling blocks we saw is that huge gap between the IT and OT, they don't talk. You talk about the telco, that telco role doesn't tie in to the traditional data center world. It's at the edge and some expert comes in and does their piece but, you know, smashing these worlds together is a real challenge. >> What's interest-- >> Oh, I'm sorry. >> I was going to say 5G is the technology that I think is going to create the catalyst for those technologies to come together, right? So you have the enterprise edge, you have the industrial edge, and you have the telco edge. And over time, the more the telcos start pushing compute out to their edge, enterprise push compute out to their edge. And then you have all of these industrial IoT devices. The definition of the edge is going to begin to blur. >> I think this is, I think the IoT, industrial IoT, is probably the most important tech story this generation. It doesn't get as much play as AI, 'cause AI kind of sounds cooler, attracts young kids to be coders, but IoT is really the most important thing because think about the industrial IoT, the threats, cyber threats, cyber security. One hole, one hole and the attacker is in. Just to speak security and critical. >> I actually think it's beyond that because I don't know if you heard Pat talk about his definition of the edge, which is actually that merging of the digital and physical worlds. When you think about that, most of human problems can be solved by great technology, technology for good. And so you think about industries being pushed to produce more, 70% more food with just 5% extra land, or you know, carbon emissions, all of these problems which with good visibility control and management can be solved and that's really what we're trying to do-- >> Yeah, but good intentions, I understand where Pat's coming from. It's good, it's good marketing on the stage but the reality is, is when you roll out the tech to make that happen, if you don't have that security intrinsically pulled in, this means that you got to have the zero trust. But IoT is a different animal on a thin edge, than it is, say a data center. So like, it's just one of those things where we're watching 'cause it's just, there's so many, the service area is so large. >> Yeah, and in fact, one of the things that we're doing in terms of incorporating security in the management is looking at hardware Root of Trust right down to every device that's managed and being able to, you know, to attest whether something is legitimate or not. So we're rolling all of those things into our technologies. >> So, Pat brought up the telco. Earlier on, we were asking some of our guests about the business model on telco, because, you know, telcos have been struggling, they had owned infrastructure. So when you own infrastructure, it's hard to go out of business unless you actually run out of cash, but they had plenty of working capital, but they got to get their business model. You guys have any thoughts on as telco starts to modernize, whether they migrate and modernize or modernize and migrate with cloud, what's hopeful things that you can share that's showing business models for telco? Because 5G, someone's got to pay for it. It's not inexpensive to roll out 5G. >> So, what we're seeing with our telco customers is that they're finally beginning to realize that they can actually accelerate their time of revenue with new services, with a software defined infrastructure. So, I think when first we met, you know, we were in the early stages of developing the market for telco with software defined. But we've crossed the chasm now to where we have over a hundred discreet telcos that are in production on our platform. And so we have proof points that says, "Okay, now they can accelerate time to new revenue". What we're focused on now is helping them extend that out to the edge. And as you know, partners with Lakshmi, we see the telcos as a route to the enterprise market for our edge an IoT solutions. Right, so there's an opportunity for telcos to participate not just in the cloud economy but the edge economy. In terms of the business models, the change is driving the business model transformation. You know, the technology is driving business model transformation. But it's an excellent point. Its operating models are transforming, business models are transforming, and interestingly enough, commercial models are transforming as well. >> Lakshmi, you know the app side's going to be where the growth is now. Getting back to the good thing, once that infrastructure is stable, the apps can come out. So the application development, the microservices, that kind of to me connects that Kubernetes piece to it. That is an opportunity to telco providers, right? >> Yeah, absolutely. I mean again, it's all about deploying and managing applications right at the edge and so the infrastructure that we're building, with all of the announcements that you heard and the features that we're adding into the product profile is really about how do you deploy and manage these applications right down at the device level and that's really where I think it's going to transform. >> A lot of action. >> A study came out just yesterday that the edge market is targeted to be a $4.1 trillion market. >> Yeah, it's going to be huge. >> That's trillion with a T. >> Yeah, it's going to be huge. >> So, wondering what you can say about the ecosystem. Because, you know we've looked, VMware has always had ecosystems but it's many ecosystems, and you've got a cloud marketplace, and there's lots of different customers so will some of your existing partners go along with this, is it building out a new suite, you know, when you look at the edge and IoT? >> I think there will be a group of partners that come along, for sure, but, you know, IoT, especially when you think about industrial IoT, it is a new space of players and we're building that ecosystem and trying to figure out what customers want, right? Because, it's an ocean, you could boil it but that may not be the right approach. >> Yeah, I mean, it's like you said, there's a T on the TAM. It's a huge, huge TAM. It's going to be a huge application boom and IT culture's got to evolve from that perimeter-based security to a surface area that's out there, that's one light bulb on a factory, that IP enabled, could be a malware entry point. It could be something for a worm to get in there. >> Well, it's really like any device. What's that, any-- >> Any device, any application, any device, any Cloud. >> Any cloud, I think in IoT, it's anywhere. >> Anywhere, exactly. >> Totally, totally. >> And to your very accurate point about the security associated with that, right? In the telcos, actually owning that last mile. Right, so when we talk about $4.1 trillion of opportunity, and the need to develop an ecosystem that can support those edge and IoT solutions, the telcos really are in the cat seat to take advantage of that because they own that last mile of customer access, customer influence, they own the cell towers. Right, so as we push compute out to the radio access, telcos have an opportunity to participate. >> Honore, I want to get your thoughts while you're here and Lakshmi, if you can chime in, that's cool too. I'm doing a big editorial on industrial IoT national security. This all kind of leads into policy, potential regulation. You know, I mentioned tech for good, tech for bad is neutral how it's shaped. I'm assuming you guys are going to take a shape in some of those conversations. Any thoughts on regulatory things happening because with cyber security, cyber war that's happening on our digital turf, the telcos are in a prime position to assist and help shape that, you guys can do that. Any thoughts on how you see that, that conversation? Anything you'd like to add? >> So VMware is participating in consortiums associate with those very topics. And of course we are developing technology with an appreciation and understanding respect for the governing agencies across every country as it relates to privacy and security. And so I'm sure, you know and it varies from country to country. In terms of what data you have access to and how you deliver that data and what you do with that data, that's a really hot topic in Washington these days, right? >> And software helps too. >> Software does help, right? You have so much flexibility with software but at the same time you have so much risk that you have to prevent. What we've learned is, it's really about the individual's information. Whether that is a device or an industrial device or an end user or a potentially, a point of presence. It really does depend on what you do with that data, who touches the data, and where is that data going to be housed. And so each of the different countries, each of the different telcos, depending on their location are adhering to the governmental requirements for who does what with the data. >> Yeah, it's interesting, we just did a power panel in our studio, we had experts come in talking about called the "Cybergeddon" scenario, which is a hacker taking over not just malware and getting penetrated with worms and getting access to data, but actually taking over physical devices to harm people. So, this is kind of a nation threat thing. It's not so much a corporate thing, but you know, there is a shaping opportunity here when we're trying to identify where, you know, good governance, at least from a policy stand point, tech are coming together. More and more, it's happening. >> And of course, we participate very actively here in the U.S., right? Because we are a U.S. headquartered company. We try to participate where we can in some of the other countries for the regulatory agencies. And we're a part of the world economic forum. So through that vehicle, you know, through that consortium we're also trying to influence, for good, of course. We just recently, we announced this morning that we acquired Uhana and Uhana is an artificial intelligence machine learning and specific to telco, it will observe, analyze and report back on data all the way to the consumer level across a radio access network. And the one question we get asked from every telco that we do business with is, "What do you do with the data?" And of course, we don't do anything with the data. In that particular technology, we're observing it but we don't necessarily touch it. But you're exactly right, I think it's something that's going to be a hot topic for a time, awhile to come. >> It's an opportunity for tech for good. Guys, thanks for coming on, sharing your insights. Great to see you again, thanks for coming on. Great insights, a lot changing and certainly very relevant, the IoT Edge, telco, IoT's all happening, AI is a part of it. It's theCUBE, live coverage. I'm John Furrier with Stu Miniman. Be right back after this short break. (light techno music)

>> Live from San Francisco, California, it's theCUBE covering the IBM Chief Data Officers Summit, brought to you by IBM. >> Welcome back to San Francisco everybody. I'm Dave Vellante and you're watching theCUBE, the leader in live tech coverage. This is the end of the day panel at the IBM Chief Data Officer Summit. This is the 10th CDO event that IBM has held and we love to to gather these panels. This is a data all-star panel and I've recruited Seth Dobrin who is the CDO of the analytics group at IBM. Seth, thank you for agreeing to chip in and be my co-host in this segment. >> Yeah, thanks Dave. Like I said before we started, I don't know if this is a promotion or a demotion. (Dave laughing) >> We'll let you know after the segment. So, the data all-star panel and the data all-star awards that you guys are giving out a little later in the event here, what's that all about? >> Yeah so this is our 10th CDU Summit. So two a year, so we've been doing this for 5 years. The data all-stars are those people that have been to four at least of the ten. And so these are five of the 16 people that got the award. And so thank you all for participating and I attended these like I said earlier, before I joined IBM they were immensely valuable to me and I was glad to see 16 other people that think it's valuable too. >> That is awesome. Thank you guys for coming on. So, here's the format. I'm going to introduce each of you individually and then ask you to talk about your role in your organization. What role you play, how you're using data, however you want to frame that. And the first question I want to ask is, what's a good day in the life of a data person? Or if you want to answer what's a bad day, that's fine too, you choose. So let's start with Lucia Mendoza-Ronquillo. Welcome, she's the Senior Vice President and the Head of BI and Data Governance at Wells Fargo. You told us that you work within the line of business group, right? So introduce your role and what's a good day for a data person? >> Okay, so my role basically is again business intelligence so I support what's called cards and retail services within Wells Fargo. And I also am responsible for data governance within the business. We roll up into what's called a data governance enterprise. So we comply with all the enterprise policies and my role is to make sure our line of business complies with data governance policies for enterprise. >> Okay, good day? What's a good day for you? >> A good day for me is really when I don't get a call that the regulators are knocking on our doors. (group laughs) Asking for additional reports or have questions on the data and so that would be a good day. >> Yeah, especially in your business. Okay, great. Parag Shrivastava is the Director of Data Architecture at McKesson, welcome. Thanks so much for coming on. So we got a healthcare, couple of healthcare examples here. But, Parag, introduce yourself, your role, and then what's a good day or if you want to choose a bad day, be fun the mix that up. >> Yeah, sounds good. Yeah, so mainly I'm responsible for the leader strategy and architecture at McKesson. What that means is McKesson has a lot of data around the pharmaceutical supply chain, around one-third of the world's pharmaceutical supply chain, clinical data, also around pharmacy automation data, and we want to leverage it for the better engagement of the patients and better engagement of our customers. And my team, which includes the data product owners, and data architects, we are all responsible for looking at the data holistically and creating the data foundation layer. So I lead the team across North America. So that's my current role. And going back to the question around what's a good day, I think I would say the good day, I'll start at the good day. Is really looking at when the data improves the business. And the first thing that comes to my mind is sort of like an example, of McKesson did an acquisition of an eight billion dollar pharmaceutical company in Europe and we were creating the synergy solution which was based around the analytics and data. And actually IBM was one of the partners in implementing that solution. When the solution got really implemented, I mean that was a big deal for me to see that all the effort that we did in plumbing the data, making sure doing some analytics, is really helping improve the business. I think that is really a good day I would say. I mean I wouldn't say a bad day is such, there are challenges, constant challenges, but I think one of the top priorities that we are having right now is to deal with the demand. As we look at the demand around the data, the role of data has got multiple facets to it now. For example, some of the very foundational, evidentiary, and compliance type of needs as you just talked about and then also profitability and the cost avoidance and those kind of aspects. So how to balance between that demand is the other aspect. >> All right good. And we'll get into a lot of that. So Carl Gold is the Chief Data Scientist at Zuora. Carl, tell us a little bit about Zuora. People might not be as familiar with how you guys do software for billing et cetera. Tell us about your role and what's a good day for a data scientist? >> Okay, sure, I'll start by a little bit about Zuora. Zuora is a subscription management platform. So any company who wants to offer a product or service as subscription and you don't want to build your billing and subscription management, revenue recognition, from scratch, you can use a product like ours. I say it lets anyone build a telco with a complicated plan, with tiers and stuff like that. I don't know if that's a good thing or not. You guys'll have to make up your own mind. My role is an interesting one. It's split, so I said I'm a chief data scientist and we work about 50% on product features based on data science. Things like churn prediction, or predictive payment retries are product areas where we offer AI-based solutions. And then but because Zuora is a subscription platform, we have an amazing set of data on the actual performance of companies using our product. So a really interesting part of my role has been leading what we call the subscription economy index and subscription economy benchmarks which are reports around best practices for subscription companies. And it's all based off this amazing dataset created from an anonymized data of our customers. So that's a really exciting part of my role. And for me, maybe this speaks to our level of data governance, I might be able to get some tips from some of my co-panelists, but for me a good day is when all the data for me and everyone on my team is where we left it the night before. And no schema changes, no data, you know records that you were depending on finding removed >> Pipeline failures. >> Yeah pipeline failures. And on a bad day is a schema change, some crucial data just went missing and someone on my team is like, "The code's broken." >> And everybody's stressed >> Yeah, so those are bad days. But, data governance issues maybe. >> Great, okay thank you. Jung Park is the COO of Latitude Food Allergy Care. Jung welcome. >> Yeah hi, thanks for having me and the rest of us here. So, I guess my role I like to put it as I'm really the support team. I'm part of the support team really for the medical practice so, Latitude Food Allergy Care is a specialty practice that treats patients with food allergies. So, I don't know if any of you guys have food allergies or maybe have friends, kids, who have food allergies, but, food allergies unfortunately have become a lot more prevalent. And what we've been able to do is take research and data really from clinical trials and other research institutions and really use that from the clinical trial setting, back to the clinical care model so that we can now treat patients who have food allergies by using a process called oral immunotherapy. It's fascinating and this is really personal to me because my son as food allergies and he's been to the ER four times. >> Wow. >> And one of the scariest events was when he went to an ER out of the country and as a parent, you know you prepare your child right? With the food, he takes the food. He was 13 years old and you had the chaperones, everyone all set up, but you get this call because accidentally he ate some peanut, right. And so I saw this unfold and it scared me so much that this is something I believe we just have to get people treated. So this process allows people to really eat a little bit of the food at a time and then you eat the food at the clinic and then you go home and eat it. Then you come back two weeks later and then you eat a little bit more until your body desensitizes. >> So you build up that immunity >> Exactly. >> and then you watch the data obviously. >> Yeah. So what's a good day for me? When our patients are done for the day and they have a smile on their face because they were able to progress to that next level. >> Now do you have a chief data officer or are you the de facto CFO? >> I'm the de facto. So, my career has been pretty varied. So I've been essentially chief data officer, CIO, at companies small and big. And what's unique about I guess in this role is that I'm able to really think about the data holistically through every component of the practice. So I like to think of it as a patient journey and I'm sure you guys all think of it similarly when you talk about your customers, but from a patient's perspective, before they even come in, you have to make sure the data behind the science of whatever you're treating is proper, right? Once that's there, then you have to have the acquisition part. How do you actually work with the community to make sure people are aware of really the services that you're providing? And when they're with you, how do you engage them? How do you make sure that they are compliant with the process? So in healthcare especially, oftentimes patients don't actually succeed all the way through because they don't continue all the way through. So it's that compliance. And then finally, it's really long-term care. And when you get the long-term care, you know that the patient that you've treated is able to really continue on six months, a year from now, and be able to eat the food. >> Great, thank you for that description. Awesome mission. Rolland Ho is the Vice President of Data and Analytics at Clover Health. Tell us a little bit about Clover Health and then your role. >> Yeah, sure. So Clover is a startup Medicare Advantage plan. So we provide Medicare, private Medicare to seniors. And what we do is we're because of the way we run our health plan, we're able to really lower a lot of the copay costs and protect seniors against out of pocket. If you're on regular Medicare, you get cancer, you have some horrible accident, your out of pocket is infinite potentially. Whereas with Medicare Advantage Plan it's limited to like five, $6,000 and you're always protected. One of the things I'm excited about being at Clover is our ability to really look at how can we bring the value of data analytics to healthcare? Something I've been in this industry for close to 20 years at this point and there's a lot of waste in healthcare. And there's also a lot of very poor application of preventive measures to the right populations. So one of the things that I'm excited about is that with today's models, if you're able to better identify with precision, the right patients to intervene with, then you fundamentally transform the economics of what can be done. Like if you had to pa $1,000 to intervene, but you were only 20% of the chance right, that's very expensive for each success. But, now if your model is 60, 70% right, then now it opens up a whole new world of what you can do. And that's what excites me. In terms of my best day? I'll give you two different angles. One as an MBA, one of my best days was, client calls me up, says, "Hey Rolland, you know, "your analytics brought us over $100 million "in new revenue last year." and I was like, cha-ching! Excellent! >> Which is my half? >> Yeah right. And then on the data geek side the best day was really, run a model, you train a model, you get ridiculous AUC score, so area under the curve, and then you expect that to just disintegrate as you go into validation testing and actual live production. But the 98 AUC score held up through production. And it's like holy cow, the model actually works! And literally we could cut out half of the workload because of how good that model was. >> Great, excellent, thank you. Seth, anything you'd add to the good day, bad day, as a CDO? >> So for me, well as a CDO or as CDO at IBM? 'Cause at IBM I spend most of my time traveling. So a good day is a day I'm home. >> Yeah, when you're not in an (group laughing) aluminum tube. >> Yeah. Hurdling through space (laughs). No, but a good day is when a GDPR compliance just happened, a good day for me was May 20th of last year when IBM was done and we were, or as done as we needed to be for GDPR so that was a good day for me last year. This year is really a good day is when we start implementing some new models to help IBM become a more effective company and increase our bottom line or increase our margins. >> Great, all right so I got a lot of questions as you know and so I want to give you a chance to jump in. >> All right. >> But, I can get it started or have you got something? >> I'll go ahead and get started. So this is a the 10th CDO Summit. So five years. I know personally I've had three jobs at two different companies. So over the course of the last five years, how many jobs, how many companies? Lucia? >> One job with one company. >> Oh my gosh you're boring. (group laughing) >> No, but actually, because I support basically the head of the business, we go into various areas. So, we're not just from an analytics perspective and business intelligence perspective and of course data governance, right? It's been a real journey. I mean there's a lot of work to be done. A lot of work has been accomplished and constantly improving the business, which is the first goal, right? Increasing market share through insights and business intelligence, tracking product performance to really helping us respond to regulators (laughs). So it's a variety of areas I've had to be involved in. >> So one company, 50 jobs. >> Exactly. So right now I wear different hats depending on the day. So that's really what's happening. >> So it's a good question, have you guys been jumping around? Sure, I mean I think of same company, one company, but two jobs. And I think those two jobs have two different layers. When I started at McKesson I was a solution leader or solution director for business intelligence and I think that's how I started. And over the five years I've seen the complete shift towards machine learning and my new role is actually focused around machine learning and AI. That's why we created this layer, so our own data product owners who understand the data science side of things and the ongoing and business architecture. So, same company but has seen a very different shift of data over the last five years. >> Anybody else? >> Sure, I'll say two companies. I'm going on four years at Zuora. I was at a different company for a year before that, although it was kind of the same job, first at the first company, and then at Zuora I was really focused on subscriber analytics and churn for my first couple a years. And then actually I kind of got a new job at Zuora by becoming the subscription economy expert. I become like an economist, even though I don't honestly have a background. My PhD's in biology, but now I'm a subscription economy guru. And a book author, I'm writing a book about my experiences in the area. >> Awesome. That's great. >> All right, I'll give a bit of a riddle. Four, how do you have four jobs, five companies? >> In five years. >> In five years. (group laughing) >> Through a series of acquisition, acquisition, acquisition, acquisition. Exactly, so yeah, I have to really, really count on that one (laughs). >> I've been with three companies over the past five years and I would say I've had seven jobs. But what's interesting is I think it kind of mirrors and kind of mimics what's been going on in the data world. So I started my career in data analytics and business intelligence. But then along with that I had the fortune to work with the IT team. So the IT came under me. And then after that, the opportunity came about in which I was presented to work with compliance. So I became a compliance officer. So in healthcare, it's very interesting because these things are tied together. When you look about the data, and then the IT, and then the regulations as it relates to healthcare, you have to have the proper compliance, both internal compliance, as well as external regulatory compliance. And then from there I became CIO and then ultimately the chief operating officer. But what's interesting is as I go through this it's all still the same common themes. It's how do you use the data? And if anything it just gets to a level in which you become closer with the business and that is the most important part. If you stand alone as a data scientist, or a data analyst, or the data officer, and you don't incorporate the business, you alienate the folks. There's a math I like to do. It's different from your basic math, right? I believe one plus one is equal to three because when you get the data and the business together, you create that synergy and then that's where the value is created. >> Yeah, I mean if you think about it, data's the only commodity that increases value when you use it correctly. >> Yeah. >> Yeah so then that kind of leads to a question that I had. There's this mantra, the more data the better. Or is it more of an Einstein derivative? Collect as much data as possible but not too much. What are your thoughts? Is more data better? >> I'll take it. So, I would say the curve has shifted over the years. Before it used to be data was the bottleneck. But now especially over the last five to 10 years, I feel like data is no longer oftentimes the bottleneck as much as the use case. The definition of what exactly we're going to apply to, how we're going to apply it to. Oftentimes once you have that clear, you can go get the data. And then in the case where there is not data, like in Mechanical Turk, you can all set up experiments, gather data, the cost of that is now so cheap to experiment that I think the bottleneck's really around the business understanding the use case. >> Mm-hmm. >> Mm-hmm. >> And I think the wave that we are seeing, I'm seeing this as there are, in some cases, more data is good, in some cases more data is not good. And I think I'll start it where it is not good. I think where quality is more required is the area where more data is not good. For example like regulation and compliance. So for example in McKesson's case, we have to report on opioid compliance for different states. How much opioid drugs we are giving to states and making sure we have very, very tight reporting and compliance regulations. There, highest quality of data is important. In our data organization, we have very, very dedicated focus around maintaining that quality. So, quality is most important, quantity is not if you will, in that case. Having the right data. Now on the other side of things, where we are doing some kind of exploratory analysis. Like what could be a right category management for our stores? Or where the product pricing could be the right ones. Product has around 140 attributes. We would like to look at all of them and see what patterns are we finding in our models. So there you could say more data is good. >> Well you could definitely see a lot of cases. But certainly in financial services and a lot of healthcare, particularly in pharmaceutical where you don't want work in process hanging around. >> Yeah. >> Some lawyer could find a smoking gun and say, "Ooh see." And then if that data doesn't get deleted. So, let's see, I would imagine it's a challenge in your business, I've heard people say, "Oh keep all the, now we can keep all the data, "it's so inexpensive to store." But that's not necessarily such a good thing is it? >> Well, we're required to store data. >> For N number of years, right? >> Yeah, N number of years. But, sometimes they go beyond those number of years when there's a legal requirements to comply or to answer questions. So we do keep more than, >> Like a legal hold for example. >> Yeah. So we keep more than seven years for example and seven years is the regulatory requirement. But in the case of more data, I'm a data junkie, so I like more data (laughs). Whenever I'm asked, "Is the data available?" I always say, "Give me time I'll find it for you." so that's really how we operate because again, we're the go-to team, we need to be able to respond to regulators to the business and make sure we understand the data. So that's the other key. I mean more data, but make sure you understand what that means. >> But has that perspective changed? Maybe go back 10 years, maybe 15 years ago, when you didn't have the tooling to be able to say, "Give me more data." "I'll get you the answer." Maybe, "Give me more data." "I'll get you the answer in three years." Whereas today, you're able to, >> I'm going to go get it off the backup tapes (laughs). >> (laughs) Yeah, right, exactly. (group laughing) >> That's fortunately for us, Wells Fargo has implemented data warehouse for so many number of years, I think more than 10 years. So we do have that capability. There's certainly a lot of platforms you have to navigate through, but if you are able to navigate, you can get to the data >> Yeah. >> within the required timeline. So I have, astonished you have the technology, team behind you. Jung, you want to add something? >> Yeah, so that's an interesting question. So, clearly in healthcare, there is a lot of data and as I've kind of come closer to the business, I also realize that there's a fine line between collecting the data and actually asking our folks, our clinicians, to generate the data. Because if you are focused only on generating data, the electronic medical records systems for example. There's burnout, you don't want the clinicians to be working to make sure you capture every element because if you do so, yes on the back end you have all kinds of great data, but on the other side, on the business side, it may not be necessarily a productive thing. And so we have to make a fine line judgment as to the data that's generated and who's generating that data and then ultimately how you end up using it. >> And I think there's a bit of a paradox here too, right? The geneticist in me says, "Don't ever throw anything away." >> Right. >> Right? I want to keep everything. But, the most interesting insights often come from small data which are a subset of that larger, keep everything inclination that we as data geeks have. I think also, as we're moving in to kind of the next phase of AI when you can start doing really, really doing things like transfer learning. That small data becomes even more valuable because you can take a model trained on one thing or a different domain and move it over to yours to have a starting point where you don't need as much data to get the insight. So, I think in my perspective, the answer is yes. >> Yeah (laughs). >> Okay, go. >> I'll go with that just to run with that question. I think it's a little bit of both 'cause people touched on different definitions of more data. In general, more observations can never hurt you. But, more features, or more types of things associated with those observations actually can if you bring in irrelevant stuff. So going back to Rolland's answer, the first thing that's good is like a good mental model. My PhD is actually in physical science, so I think about physical science, where you actually have a theory of how the thing works and you collect data around that theory. I think the approach of just, oh let's put in 2,000 features and see what sticks, you know you're leaving yourself open to all kinds of problems. >> That's why data science is not democratized, >> Yeah (laughing). >> because (laughing). >> Right, but first Carl, in your world, you don't have to guess anymore right, 'cause you have real data. >> Well yeah, of course, we have real data, but the collection, I mean for example, I've worked on a lot of customer churn problems. It's very easy to predict customer churn if you capture data that pertains to the value customers are receiving. If you don't capture that data, then you'll never predict churn by counting how many times they login or more crude measures of engagement. >> Right. >> All right guys, we got to go. The keynotes are spilling out. Seth thank you so much. >> That's it? >> Folks, thank you. I know, I'd love to carry on, right? >> Yeah. >> It goes fast. >> Great. >> Yeah. >> Guys, great, great content. >> Yeah, thanks. And congratulations on participating and being data all-stars. >> We'd love to do this again sometime. All right and thank you for watching everybody, it's a wrap from IBM CDOs, Dave Vellante from theCUBE. We'll see you next time. (light music)

>> Live from Barcelona, Spain. It's the cue covering Sisqo. Live Europe, Brought to you by Cisco and its ecosystem partners. >> Welcome back to Barcelona, Everybody watching the Cube, the leader in live tech coverage. My name is Dave Valentin here with my co host Student events. Do Myself and John for Be here all week. Eric Hurt, Saugus Here Long time Cuba Long friend. Great to see you again. He's the CMO of IBM IBM Storage division. He's joined by James Amy's, who's the head of networks at advance. The service provider Guys, Welcome to the Cube. Good to see again. >> Great. Thanks for having us loved being on the cute. >> So we love having you So, James, let's start with you. Tell us a little bit about advanced to want to dig into some of the networking trends. We're hearing a lot about it here. It's just go live. >> Yeah, I think so. Advanced are a manage service provider software software company based in the UK, one of the largest software companies in the UK, providing interim solutions for lots of different Marchal market verticals, including healthcare, local government, regional government, national infrastructure projects we've got involved with as well as charity sector legal sector. A lot of education work we do is real diverse portfolio of products we offer on with the manage services piece. We also offer complete outsourcing. So this is desktop support. Telephony support, printer support all the >> way back into integration with public cloud platforms and private cloud platforms, the majority of >> which is our in. >> So so Eric advanced are both a customer and a partner, right? Right, right. And so you love you. Love versus Stack. These guys are presumed versus stack customers. Well >> stacked customer in the Versace tack, as you know, Integrate. Cisco, UCS, Cisco Networking Infrastructure, IBM Storage of all types entry products up into the fastest all flash raise with our software spectrum virtualized spectrum, Accelerate Family and James's company is using versus tax is part of their infrastructure, which they then offer, as you know, to a service toe and uses. James just described. >> So let's talk about some of the big trends that you guys are seeing and how you're both responding to customers and you're responding to your customer. So we're seeing two hearing today. Lot about multi cloud. We've been hearing that for a while the network is flattening your network expert love to get your your thoughts on that. Security, obviously, is a huge topic. End end management, another big topic, something that IBM is focused on. So so James, what 1 of the big mega trends that you're seeing that a driving your business decisions and your customer's activity? One >> of the big changes we're seeing is a change from large scare enterprise scale deployments off a particular type of technology on customers are now choosing because they're informed the best fit for a particular application or particular service on that may be coming to a service provider like ourselves to offer our services products to them. Uh, or they're looking for us to run in infrastructure service for them or integrate with a public cloud offering. So the competition of the public cloud for service providers is key on DH. I think people were looking around a few years ago thinking, How do we compete to this well, with partnerships that we have in our Francisco? It gives us a very compelling competitive offering. But we can turn around and say, Well, we can give you a like for like, but we can give you a slightly better service because we could give you guaranteed availability. We give you guaranteed price point on, and this is all backed with key vendor certified designs. So we're not talking about going out on developing a solution that takes maybe eighteen months to take to market. This is understanding a requirement for a quick, you know, Q and A with a customer a line that, too a reference architecture that we can literally just pick up off the shelf, deploy into our data centers using the standard building rocks that we use across the business. So Nexus nine K seven k's or our standard bread and butter inside the data center environment. As Eric pointed out, Cisco UCS is our our key Intel computer platform that we used on DH. The store wise IBM product has been a real true success story for us. So we started off being a a mixed then the house where we would align storage requirement paste with what we could find in the market. That was, that was a good fit. But the store was products is basically just allowed us to standardize on the speed of deployment is one of the key things. So we started out with a very lengthy lead time tio service ready, which is when we start charging for revenue on if we want a ninety day build. Well, we've got a lot of special service time, A lot of engineering time getting that ready Teo, Teo and take to the customer and then we turn it on. We can start seeing revenue from that platform with versus Stack. This enabled us to accelerate how quickly we can turn that on. And we've seen that drop, too. They're literally days through standardisation elements of automation as well. Many of our environments are bespoke because we have such a wide arrange off different types of customers with different needs, but it allows us to take those standing building blocks, align them to their needs and deliver that service. >> James James, we found the peas are often in the middle of those discussions that customers are having on multi clouds. You talked a lot about the services you build. Are they also coming to you? If if you tie into the public Cloud services or yes, maybe you can help explain a little bit on how that worked Five years ago, it was the public loud there are going to kill them and service providers. And what we see is customers can't sort out half of what's going on. They've got to be able to turn two partners like you to be able to figure this out. >> Yeah, that's a fantastic question. I think three years ago we'd be talking to our customers and they were I am going to this public cloud or I am going to build this infrastructure. Where is now? They're They're making Mohr informed select decisions based on the drive to the hosted office and voice platforms offered by Microsoft. There's a big driving. Many of our customers are going in that direction, but it's how we integrate that with legacy applications. Some of the solutions that some of our customers use have have have had millions of pounds of investment into them, and that's not something I can just turn off the water away from overnight. So it is how we're integrating that. We're doing that at the network level, so it's how we're appearing with different service providers, bringing that in integrating that, I'm offering it to them as a solution. What we try tio, we try to try position ourselves is really it's the same experience, regardless of where we're placing it. Consumption. Workload doesn't know whether it's inside our data centers, whether we're talking one of the public cloud platforms or even on premise. So we have quite a few customers that still have significant presence on premises because that's right for their business, depending on on what they're doing, especially some of the research scientists. >> So you've got to deliver flexibility in your architecture, and you talk a lot about software to find you guys made a big move to software to find, you know, a couple years ago, actually, maybe discuss how that fits in to how you're servicing advanced another client? >> Sure. So you know, IBM Storage has embraced multi Cloud for several years. So our solutions. While, of course, they work with IBM, Cloud and IBM cloud private work with Amazon. They work with azure Google Cloud and in fact, some are products. For example, the versus stack not only is advanced using it, but we've got pry forty or fifty public, small, medium sized cloud providers that our public references for the vs Tag and Spectrum Protect you Know which is our backup product Number one in the Enterprise. Back up space Expect from detectives Got at least three hundred cloud providers. Medium, small and big. Who offered the engine underneath for their backup is a service is spectrum protect, So we make sure that weather PR transparent cloud tearing our cyber resiliency technology. What we doing? Backup archive object storage works with essentially all cloud providers. That way, someone like James A. CSP MSP can leverage our products. And we, like I said, we have tons of public records around versus Stack for that, but so can an enterprise. And in fact, I saw survey recently that it was done in Europe and in North America that when you look at a roughly two billion US size revenue and up the average company of that sizing up, we use five different public cloud riders at one time. Where that it be due to legal reasons whether that be procurement. You know, the Web is really the Internet. And, yeah, Cloud is really just It's been around for twenty some years. So in bigger accounts, guess what is now involved Procurement Well, we love that you did that deal with IBM club, but you are going to get a competitive quote now from Amazon and Microsoft, right? So that's driven it legal's driven it. Certain countries, right? The data needs to stay in that country, even if your cloud if eyeing it, it's so to speak. So if the clap water doesn't have a data center there, guess what? Another geographer used different. And then you, of course, still have some large entities that still allow regional buying pattern so they'll have three or four different cloud providers that air quote certified by corporate. And then you could use whichever one you want, so we make sure that we could take advantage of that. Wade and IBM. We ride the wave, We don't fight the way. >> So you've got in that situation. You these multi cloud you got different AP eyes, You get different frameworks potty, you abstract all that complexity you got, Francisco coming at it from a networking standpoint, I b m. Now with Red Hat is good. Be a big player in that that world. VM where What do you guys do? James, in terms of of simplifying all that multi cloud complexity >> for people. I think some of it is actually the mystifying on its engaging with our partners to understand what the proposition is on, how we can develop that on a line, that to mind your own business, but more importantly, to the needs of our customers. We've got some really, really talented technicians worked within within advance, and we've got a number of different forums that allow them to feed back their ideas. But we've got the alignments between those partners and and some of those communities, so that we can have an open discussion on drive. Some of that thinking forward about ultimately see engaging with customers. So the customers feedback is key on how we shape and deliver no need service to them, but also to the service to other customers. We have a number of customers that are very similar, but they may work in different spaces, some somewhere even competitive. So we have to tread that line very safe, very carefully and safely. But it is. It's a good one to one relationship between the client service managers, technical technicians. We have inside business having that to complete three sixty communication is key, but that's that's that's really the bottom takes. Its creation >> came like youto dig into security for us a little bit. You know, I think we surpassed a couple of years ago. I'm not going to go to the cloud to it because it's not secure to Oh, I understand it's time for me to least reevaluate meant security and, most likely, you know, manage service fighters. Public clouds are probably more secure than what I had in my data center, but if I've got multiple environment, there's a lot of complexity there. So how do you traverse that? Make sure that you've got a comprehensive security practice, not just all these point solutions for security all over the place. >> Ah, so that's that comes onto visibility. So its visibility understanding where all the control points are within a given infrastructure on how the landscape looks. So we were working quite closely with a number actually of key Cisco and IBM partners, as well as IBM and Cisco themselves directly tohave a comprehensive offering that allows us to position to our customers. You used to once upon a time you had one game, right? So we need it is from good security on your Internet. Facing viable For now, you might have a ten. Twenty, thirty of those. We need tohave consistent policies across those. We need to understand how they're performing, but also potentially, if there's any attempt attack vector on one of them. How that how someone is trying to looking to compromise that so centralized intelligence on That's where we start to look at my eye operations to gather all that information. The long gone are the days where you have twenty people sharing a room just reading streams. Those twenty people now need thio. See reams and reams of information instantly. Something needs to be called up to them. They could make a decision quickly on Active planet on DH. That's really where we we're positioning ourselves in the market to differentiate. I'm working with key part, Mr >> Never talk about your announcement cadence. Good idea as a big show. Think coming up in a couple weeks cubes gonna be there. Of course. What can we expect from from you guys? >> So we're actually gonna announce on the fifth before things way, want to drive end users and our business partners to storage campus, which is one of the largest campuses at IBM, think we'll have over fifteen pedestals of demo and actually multiple demos because we have such a broad portfolio, from the all flash arrays to our versus stack offering to a whole set of modern data protection management control for storage, which manages in control storage, that's not ours, right? Our competitors storage as well, and, of course, our software to find storage. So we're going to do a big announcement. The focus of that will be around our storage solutions. These air solutions blueprints reference architectures is Jane, you mentioned that use our software and our storage systems that allow reseller or end user to configure systems easily. Think of it as the ultimate wrestling recipe for that German chocolate cake. But it's the perfect recipe. It's tried. It's true, it's tested. It's been on the Food Channel twenty seven times and everybody loves it. That's what we do with our our solutions. Blueprints. We'll have some announcements around modern data protection, and obviously a big focus of IBM. Storage is been in the space. So both storage as an Aye aye platform for aye aye, applications are workloads but also the incorporation of technology into our own storage systems and software. So be having announcements around that on February fifth going into think, which will then be the week after in San Francisco. >> Great. So I'm here and trusted data protection plays into that. Aye, aye. Intelligence machine intelligence. And I'm also hearing header of Geneti multiple platforms. Whether it's your storage, you said our competitors now does that also include sort of the clouds? Fear we're not announcing anything. But you guys have you know, you've seen your pictures. That's azure itt's a w a s. I mean, that continues >> so absolutely so. Whether it be what we do from backup in archive, right, let's take the easy one. So we support not only the protocol of IBM clad object storage which we acquired and allows you to have object storage either on premise or in a cloud in stance e ation. But we also support the s three protocol. So, for example, our spectrum scale software giant scale out. In fact, the two fastest supercomputers world you spectrum scale over four hundred fifty petabytes running on spectrum scale, and they continue their to an object store that supports us three. Or it can tear toe IBM clad object stories through that IBM clad object storage customer. That's great for using the S three protocol. You, Khun, Tear to that as well. That's just one example. Same thing we do for cyber resiliency. So from a cyber resents me perspective, we could do things with any cloud vendor oven air cat air gap, right? And so you could do that, eh? With tape. But you could also do that with the clouds. So if your cloud is your backup archive replication repository, then you can always roll back to a known good copy. You don't have to pay the ransom writer. When you clean up the malware, you can roll back to a known good copy, and we provide that across all of the platforms in a number of ways. Our protect family, our new products, a safeguard copy for the main friend that we announced October. So all that allows us to be multi cloud resiliency as well as how do we connect a multi cloud backup archive automated tearing all kinds of clouds, whether the IBM cloud and, of course, I'm a shareholder. So I love that, but at the same time were realistic. Lots of people use Amazon Google Azar. And like I said, there's thousands of mid two small cloud providers all over the world, and we support them, too. We engage with everyone. >> What about SAS? You know, that's one of the questions we've been trying to squint through and understand is because when you talk about five cloud providers is obviously infrastructures of service. And then there's their service providers like like Advanced. And then there's like a gazillion SAS Companies >> write a lot of data >> in there and a lot of data in there. How should we think about, you know, protecting that data? Securing that data is that sort of up to the SAS vendor, and thou shalt not touch. Or should that be part of the scope of AH, storage company? Well, so what we do >> is we engage with the SAS vendor, so we have a number of different sass coming is, in fact, one of them was on the Cube two years ago with us. They were startup in the cyber security space and all of its delivered over SAS. So what they do is in that case, the use our flash system product line, they get the performance they need to deliver south. They want no bottlenecks because obviously you have to go over the network when you're doing SAS Andi. Also, what they do is data encryption at rest. So when the data is brought in because we have on our flash arrays capability and most of our product line especially the flash systems to have no performance hit on encrypt their decrypt because its hardware embedded, they're able to have the data at rest encrypted for all their customers. That gives them a level of security when it's at rest on their site. At the same time, we've given the right performance. They need tohave soft reserve, so we engage with all we pry have three hundred, four hundred different SAS companies who are the actual software vendor and their deployment model. This software's interest, by the way, we do that as well as I mentioned, over three hundred cloud providers today have a backup is a service and the engine ease their spectrum. Protect or spectrum protect. Plus, but they may call it something else. In fact, we just had a public reference out from Silver String, which is out in the UK, and all they do is cyber resiliency. Backup in archive. That's their service. They have their own product, but then spectrum Protect and Spectrum Check plus is the engine underneath their Prada. So that's an example. In this case, the backup is a service, which, I would argue is not infrastructure, but more of an application. But then true what you call real application providers like cyber security vendors, we have a vendor who in fact, does something for all of the universities and colleges. United States. They have about eight thousand of them, including the junior colleges, and they run all their bookstores. So when you place an order, all their air NPR, everything they do is from this SAS vendor that's based in there in the Northeast. And they've got, like I said, about a thousand colleges and universities in the U. S. And Canada, and they offer this if you will bookstore as a sass service and the students use it. University uses it. And, of course, the bookstores are designed to, you know, make a little money for the university, and they all use that so that's another example. And they use are flash systems as well. And then they back up that data internally with spectrum protectors. They obviously it's the financial data as well as the inventory of all of these book stores all over the United States at the collegiate >> level right now. James Way gotta wrap, but just sort of give you the final word. UK specialist, right? So Brexit really doesn't affect you. Is that a fair statement? >> Uh, we'll do? Yes. >> How so? >> I think it's too early to tell. No one really knows. I think that's all the debates are about. AJ's trying to understand that on DH for us. We're just watching and observing. >> Staying focused on your customers, obviously. So no predictions as to what's going to happen. I was not from a weeks ago. I got hurt both sides. You know, it's definitely gonna happen, All right, Not happen, but okay, again give you the last word. You know? What's your focus? Over the next twelve eighteen months? >> Eso all our focus is really about visibility, So they they they've touched on that. We're talking about security for customers. Understanding whether data is whether exposure point saw. That's our keep. Keep focusing on DH versus stack on dh thie IBM store wise product underpin all of those offerings that we have on. That will continue to be to be so forward. >> Guys. Great to see you. Thanks so much for coming on the Cube and our pleasure hosting you. Thanks. Appreciate, Really welcome. Alright, Keep right, everybody. We'll be back. Day Volante was stew Minutemen from Cisco live in Barcelona. >> No.

>> Live from Barcelona, Spain. It's the cue covering Sisqo. Live Europe, Brought to you by Cisco and its ecosystem partners. >> Welcome back to Barcelona, Everybody watching the Cube, the leader in live tech coverage. My name is Dave Valentin here with my co host Stew, Minuteman Stew Myself and John for Be here all week. Eric Hurt, Saugus Here Long time Cuba Long friend. Great to see you again. He's the CMO of IBM IBM storage division. He's joined by James Amy's, who's the head of networks at Advanced the service provider. Guys, Welcome to the Cube. Good to see again. >> Great. Thanks for having us loved being on the cute. >> So we love having you So, James, let's start with you. Tell us a little bit about advanced to want to dig into some of the networking trends. We're hearing a lot about it here. It's just go live. >> Thanks. So Advanced are a managed service provider software company based in the UK, one of the largest software companies in the UK, providing interim solutions for lots of different market verticals, including healthcare, local government, regional government, national infrastructure projects we get involved with. As well as charity sector, legal sector. A lot of education work we do, so it's real diverse portfolio of products we offer. I'm with the managed services piece. We also offer complete IT outsourcing. So this is desktop support, telephony support, printer support all the way back into integration with public cloud platforms and private cloud platforms, the majority of which is our own >> So so Eric advanced are both a customer and a partner, right? Right, right. And so you love versus stack, these guys I presume a versus stack customers as well. >> Versus stack and customer in the Versace tack, as you know, integrates Cisco, UCS, Cisco Networking Infrastructure, IBM storage of all types entry products up into the fastest all flash raise with our software spectrum, virtualized spectrum, Accelerate Family and James's company is using versus tax is part of their infrastructure, which they then offer, as you know, to a service toe and uses. James just described. >> So let's talk about some of the big trends that you guys are seeing and how you're both responding to customers and you're responding to your customer. So we're seeing two hearing today. Lot about multi cloud. We've been hearing that for a while the network is flattening your network expert love to get your your thoughts on that. Security, obviously, is a huge topic. End end management, another big topic, something that IBM is focused on. So so James, what 1 of the big mega trends that you're seeing that a driving your business decisions and your customer's activity? One >> of the big changes we're seeing is a change from large scare enterprise scale deployments off a particular type of technology on customers are now choosing because they're informed the best fit for a particular application or particular service on that may be coming to a service provider like ourselves to offer our services products to them. Uh, or they're looking for us to roam in infrastructure service for them or integrate with a public cloud offering. So the competition of the public cloud for service providers is key. Andi, I think people were looking around a few years ago thinking, How do we compete to this well, with partnerships that we have an IBM and Cisco gives us a very compelling competitive offering, but we can turn around and say, Well, we could give you a like for like, but we can give you slightly better service because we could give you guaranteed little give you guaranteed price point on. And this is all backed with key vendor certified designs. So we're not talking about going out on developing a solution that takes maybe eighteen months to take to market. This is understanding a requirement for a quick, you know, Q and A with a customer. Our line that, too, a reference architecture that we can literally just pick up off the shelf, deploy into our data centers using the standard building brought that we use across the business so Nexus nine k seven k's or our standard bread and butter inside the data center environment. As Eric pointed out, Cisco UCS is our our key Intel computer platform that we used on DH. The store wise IBM product has been a real true success story for us. So we started off being a a mixed then the house where we would align storage requirement based with what we could find in the market. That was, that was a good fit for the store. Waste products is basically just allowed us to standardize on the speed of deployment is one of the key things. So we started out with a very lengthy lead time tio service ready, which is when we start charging for revenue on If we want a ninety day build, Well, we've got a lot of special service time, A lot of engineering time getting that ready Teo, Teo and take to the customer and then we turn it on. We can start seeing revenue from that platform with versus Stack. This enabled us to accelerate how quickly we can turn that on. And we've seen that drop, too. They're literally days through standardisation elements of automation as well. Many of our environments are bespoke because we have such a wide arrange off different types of customers with different needs, but it allows us to take those standing building blocks, align them to their needs and deliver that service. >> James James, we found the peas are often in the middle of those discussions that customers are having on multi clouds. You talked a lot about the services you build. Are they also coming to you? If if you tie into the public Cloud services or yes, maybe you can help expand a little bit on how that worked. Five years ago, it was the public loves are all going to kill the man and service providers, and what we see is customers can sort out half of what's going on. They've got to be able to turn two partners like you to be able to figure this out. >> Yeah, that's a fantastic question. I think three years ago we'd be talking to our customers and they were I am going to this public cloud or I am going to build this infrastructure. Whereas now they're making more informed select decisions based on what's right. The drive to the hosted office and voice platforms offered by Microsoft.  There's a big drive and many of our ITO customers are going in that direction, but it's how we integrate that with their legacy applications. Some of the ERP solutions that some of our customers use have had millions of pounds of investment into them, and that's not something they can just turn off and walk away  from over night. So it's how we're integrating that. We're doing that at the network level, so it's how we're pairing with different service providers, bringing that in integrating that I'm offering it to them as a solution on what we try to, we try to try and position ourselves is really it's the same experience. Regardless of where we're placing IT consumptional workload, it doesn't matter whether it's inside our data centers, whether we're talking one of the public cloud platforms or even on premise. So we have quite a few customers that still have significant presence on premise, because that's right for their business, depending on what they're doing, especially some of the research scientists. >> So you've got to deliver flexibility in your architecture, and you talk a lot about software to find you guys made it big. You move to software to find, you know, a couple years ago, actually, maybe discuss how that fits in to how you're servicing advanced another client? >> Sure. So you know, IBM Storage has embraced multi cloud for several years. No, our solutions. While, of course, they work with IBM, Cloud and IBM cloud private work with Amazon. They work with Azure Google Cloud and in fact, some are products. For example, the versus Stack not only is advanced using it, but we've got pry forty or fifty public, small, medium sized cloud providers that our public references for the versus stack and spectrum protect Now, which is our backup product number one in the Enterprise. Back up space expect from detectives Got at least three hundred cloud providers. Medium, small and big. Who offered the engine underneath for their backup is a service is spectrum protect, so we make sure that weather PR transparent cloud tearing our cyber resiliency technology. What we doing? Backup archive object storage works with essentially all cloud providers. That way, someone like James A. CSP MSP can leverage our products. And we, like I said, we have tons of public records around versus Stack for that, but so can an enterprise. And in fact, I saw survey recently, and it was done in Europe and in North America that when you look at a roughly two billion US size revenue and up the average company of that sizing up, we'll use five different public cloud riders at one time. Where that it be due to legal reasons whether that be procurement. You know, the Web is really the Internet, and go Cloud is really just It's been around for twenty some years. So in bigger accounts, guess what is now involved procurement. Well, we love that you did that deal with IBM club, but you are going to get a competitive quote now from Amazon and Microsoft, right? So that's driven it legal's driven it. Certain countries, right? The data needs to stay in that country, even if your cloud if eyeing it, it's so to speak. So the clap water doesn't have a data center there. Guess what another geographer used different. And then you, of course, still have some large entities that still allow regional buying pattern so they'll have three or four different cloud providers that air quote certified by corporate. And then you could use whichever one you want, So we make sure that we could take advantage of that. Wade and IBM We ride the wave. We don't fight the way. >> So you've got in that situation. You these multi cloud you got different AP eyes, you get different frameworks. How d'you abstract all that complexity you got, Francisco coming at it From a networking standpoint, I b m. Now with Red Hat is good. Be a big player in that that world. VM where What do you guys do? James, in terms of of simplifying all that multi cloud complexity >> for people. I think some of it is actually demystifying on DH. It's engaging with our partners to understand what the proposition is on, how we can develop that on a line that to learn their own business but more importantly, to the needs of our customers. We've got some really, really talented technicians worked within within advance, and we've got a number of different forums that allow them to feed back their ideas. We've got thie alignment between those partners on DH, some of those communities, so that we can have an open discussion and drive. Some of that thinking forward about ultimately see engaging with customers so the customers feedback is key on how we shape and deliver only in service to them, but also to the service to other customers. We have a number of customers that are very similar, but they may work in different spaces, some somewhere even competitive. So we have to tread that line very safe, very carefully and safely. But it is. It's a good one to one relationship between the client service managers technical so that the technicians we have inside business having that complete three sixty. Communication is key. That's that's that's really the bottom takes its creation >> came like youto dig into security for us a little bit. You know, I think we surpassed a couple of years ago. I'm not going to go to the cloud to it because it's not secure to Oh, I understand it's time for me to least reevaluate my security and most likely, no manage service fighters. Public clouds are probably more secure than what I had in my data center, but if I've got multiple environment, there's a lot of complexity there. So how do you traverse that? Make sure that you've got a comprehensive security practice, not just all these point solutions for security all over the place. >> Yeah, so that's that comes onto visibility. So its visibility understanding where all the control points are within a given infrastructure on how the landscape looks. So we were working quite closely with a number actually Key Cisco and IBM partners, as well as IBM and Cisco themselves directly to have a comprehensive offering that allows us to position to our customers. You used to once upon a time you had one guy, right? So we need It is from good security on your Internet. Facing viable For now, you may have a ten. Twenty, thirty of those. We need tohave consistent policies across those. We need to understand how they're performing, but also potentially, if there's any attack, attack vector on one of them. How that how someone is trying to looking to compromise that so centralized intelligence on That's where we start to look at my eye operations to gather all that information. The long gone are the days where you have twenty people sharing a room just reading streams. Those twenty people now need thio. See reams and reams of information instantly. Something needs to be called up to them. They could make a decision quickly on Active planet on DH. That's really where we were. We're We're positioning ourselves in the market to differentiate. I'm working with key partners. We have >> to do that. >> Eric, talk about your announcement cadence. That idea has a big show. Think coming up in a couple weeks. Cubes going be here? Of course. What can we expect from from you guys? >> So we're actually gonna announce on the fifth before things we want to drive end users and our business partners to storage Campus, which is one of the largest campuses at IBM, think we'll have over fifteen pedestals of demo and actually multiple demos because we have such a broad portfolio, from the all flash arrays to our versus stack offering to a whole set of modern data protection management control for storage, which manages in control storage, that's not ours, right? Our competitors storage as well. And, of course, our software to find story. So we're going to do a big announcement. The focus of that will be around our storage solutions. These air solutions blueprints reference architectures is Jane, you mentioned that use our software and our storage systems that allow reseller or end user to configure systems easily. Think of it as the ultimate wrestling recipe for that German chocolate cake. But it's the perfect recipe. It's tried. It's true, it's tested. It's been on the Food Channel twenty seven times and everybody loves it. That's what we do with our our solutions. Blueprints. We'll have some announcements around modern data protection, and obviously a big focus of IBM. Storage is been in the space. So both storage as an Aye aye platform for Aye, aye. Applications are workloads, but also the incorporation of technology into our own storage systems and software. So be having announcements around that on February fifth going into think, which will then be the week after in San Francisco. >> Great. So I'm here and trusted data protection plays into that. Aye, aye. Intelligence Machine Intelligence. And I'm also hearing header of Geneti multiple platforms. Whether it's your storage, you said our competitors now does that also include sort of the clouds Fear without announcing anything. But you guys have you know, you've seen your pictures. That's azure itt's a W a s. I mean, that continues >> so absolutely so. Whether it be what we do from backup in archive, right, let's take the easy one. So we support not only the protocol of IBM clad object storage which we acquired and allows you to have object storage either on premise or in a cloud in stance E ation. Well, we also support the s three protocol. So, for example, our spectrum scale software giant scale out. In fact, the two fastest supercomputers world use spectrum scale over four hundred fifty petabytes running on spectrum scale, and they continue ear to an object store that supports US three. Or it can tear toe IBM clad object stories through that IBM clad object storage customer. That's great using the S three protocol. You, Khun, Tear to that as well. So that's just one example. Same thing we do for cyber resiliency. So from a cyber resents me perspective, we could do things with any cloud vendor oven air cat air gap, right? And so you could do that, eh? With tape. But you could also do that with the clouds. So if your cloud is your backup archive replication repository, then you can always roll back to a known good copy. You don't have to pay the ransom writer. When you clean up the malware, you can roll back to a known good copy, and we provide that across all of the platforms in a number of ways. Our Protect family, our new products say safeguard copy for the main friend that we announced October. So all that allows us to be multi cloud resiliency as well as how do we connect a multi cloud backup archive automated tearing all kinds of clouds, whether the IBM cloud and of course, I'm a shareholder, so I love that. But at the same time, we're realistic. Lots of people use Amazon Google Azar. And like I said, there's thousands of mid two small cloud providers all over the world, and we support them, too. We engage with everyone. >> What about SAS? You know, that's one of the questions we've been trying to squint through and understand is because when you talk about five cloud providers is obviously infrastructures of service. And then there's their service providers like like Advanced. And then there's like a Brazilian SAS companies >> write a lot of data in >> there and a lot of data in there. How should we think about, you know, protecting that data? Securing that data is that sort of up to the SAS vendor, and thou shalt not touch. Or should that be part of the scope of AH, storage company? Well, so what we do >> is we engage with the SAS vendor, so we have a number of different sass coming is, in fact, one of them was on the Cube two years ago with us. They were startup in the cyber security space and all of its delivered over SAS So what they do is, in that case, the use our flash system Roddick line. They get the performance they need to deliver South. They want no bottlenecks because obviously you have to go over the network when you're doing SAS on DH, then also, what they do is data encryption at rest. So when the data is brought in because we have on our flash arrays capability and most of our product line especially the flash systems to have no performance hit on encrypt their decrypt because its hardware embedded, they're able to have the data at rest encrypted for all their customers. That gives them a level of security when it's at rest on their site. At the same time, we've given the right performance. They need tohave soft reserve, so we engage with all we pry have three hundred, four hundred different SAS companies who are the actual software vendor and their deployment model. This software's interest, by the way, we do that as well as I mentioned, over three hundred cloud providers today have a backup is a service and the engine ease their spectrum. Protect or spectrum protect. Plus, but they may call it something else. In fact, we just had a public reference out from Silver String, which is out in the UK, and all they do is cyber resiliency. Backup in archives. That's their service. They have their own product, but then spectrum Protect and Spectrum Check plus is the engine underneath their product. So that's an example. In this case, the backup is a service, which, I would argue is not infrastructure, but more of an application. But then true what you call real application providers like cyber security vendors, we have a vendor who in fact, does something for all of the universities and colleges. United States. They have about eight thousand of them, including the junior colleges, and they run all their bookstores. So when you place an order, all their air NPR, everything they do is from this SAS vendor that's based in there in the Northeast. And they've got, like I said, about a thousand colleges and universities in the U. S. And Canada, and they offer this if you will bookstore as a sass service and the students use it. University uses it. And, of course, the bookstores are designed to, you know, make a little money for the university, and they all use that. So that's another example. And they use are flash systems as well. And then they back up that data internally with spectrum protectors. They obviously it's the financial data as well as the inventory of all of these book stores all over the United States at the collegiate >> level right now. James Way gotta wrap, but just sort of give you the final word. UK specialist, right? So Brexit really doesn't affect you. Is that a fair statement >> will do? Yes. >> How so? >> I think it's too early to tell. No one really knows. I think that's that's what all the debates are about. AJ's trying to understand that on DH for us. We're just watching and observing. >> Staying focused on your customers, obviously. So no predictions as to what's going to happen. I was not from a weeks ago. I got hurt both sides. You know, it's definitely gonna happen. All right, Not happen, but up. Okay, again give you the last word. You know? What's your focus? Over the next twelve eighteen months? >> Eso all our focus is really about visibility, So they they they've touched on that. We're talking about the security for customers understanding whether data is whether exposure point saw that's all Keep keep focusing on DH versus stack on dh thie IBM store wise product underpin all of those offerings that we have on. That will continue to be to be so forward. >> Guys. Great to see you. Thanks so much for coming on the Cube and, uh, our pleasure hosting you. Thanks. Appreciate, Really welcome. All right. Keep it right there, everybody. We'll be back. Day Volante was stew Minutemen from Cisco live in Barcelona.

(upbeat music) >> Live from Bellevue, Washington, it's The Cube! Covering Smartsheet ENGAGE 18. Brought to you by, Smartsheet. >> Welcome back to the Cube's continuing coverage of Smartsheet ENGAGED 2018, their second now annual event, our first one here. I'm Lisa Martin with Jeff Frick and Jeff and I are pleased to be joined right off the keynote stage, the SVP product Gene Farrell. Gene, welcome to the Cube! >> Well, thanks for having me. I'm thrilled to be here today. >> So, packed house, this event has doubled in size from your first one last year. It's about a couple thousand people here representing 1100 companies from 20 countries and you had a really cool interactive keynote this morning where you clearly showed very, I thought, organically how much Smartsheet is collaborating with your customers to drive the innovation of Smartsheet technology. Tell us a little about some of those enhancements and how you got the crowd to burst into applause at least three times. >> Well, I, thank you, I would tell you that Smartsheet, our whole product development process is driven by the customer. 95% of what we decided to build in our roadmap is grounded in customer feedback. And so, for us anytime we can engage with customers to learn not just what do they want, but what is the problem they're trying to solve. And that's really the art of great product management is going beyond just the suggestion to how are you going to use it? How does this help your business? Because, many times there's a better way to do it than what they come up with. And so, for us, coming to this ENGAGE conference, I think we announced over 20 new capabilities that are going to be available either now or in the next couple of months. And it was really easy to know we were going to get that applause because they pretty much told us what to build. So, we looked really smart but it's actually we're just listening. And so today we launched a number of great things like multi sign to, new automations, multi-step workflow, bunch of new connectors, a really cool dashboard widget for all sorts of web content. And we just can't wait to see how customers use it. >> It's pretty interesting when you're up there because we see this at a lot of little shows, when they're just getting started and, I'll still call you guys one of the little shows, where kind of the intimacy is so much tighter and really the knowledge sharing is very, very different than when you go to a big giant show that's got tens and tens of thousands of people. And you can feel that it was palatable, I thought that was pretty brave of Mark to walk out into the crowd and stick a microphone in somebody's face, the guy responded and he had an answer to the question of the way Smartsheet has helped him and I think he had two other people. But, what's really critical, we talked about automation a lot and processes a lot but it's the scale of the simple that becomes a big challenge and that's something that squarely right in your guys' roadmap. >> Well, it's definitely what we focus on and one of the things we have as a value as a company is being authentic. And for us, regardless of however big we get we know it's important to show up and be, you know, the best of who we are and engage with our customers in a real and authentic way and so, I think that really helps us have that connection and I hope when we're a billion dollar plus revenue company that we have that same feeling. And this conference hopefully will grow to tens of thousands but I think staying grounded in customers is just critically important. And I think how we differ from maybe some other folks in the technology space is we really focus on that every day user. How do we provide practical innovation that has the power they need without all the complexity that turns them off or scares them? And that's not, it's something that nobody else really focuses on and you hear a lot from technical companies about their powerful new innovation and these great breakthrough ideas but too many times it's engineers building for technically minded folks. And we've just chosen to go after a different group. >> I'm just curious how the people usually come into Smartsheet because, you know, the workflow space and, you know, there's just so many things out there that are all about the new way to work and you know, competing for my screen that I'm working on the majority of the time. So, I'm just curious where do people find you? How do they usually enter? 'Cause you got a pretty broad suite of applications and you integrate with a lot of different desktop tools so what's kind of that process do you see? >> I would say it's probably two primary ways. First is, we still have over a 100 thousand people every month that show up at our door and sign up for a free trial. Because they're looking for a better solution. They've been living hand to mouth with email and spreadsheets and they're just kind of overwhelmed by the velocity of work that's happening, the pace of change, and the old way isn't working for them anymore. And so, a big part of how people find Smartsheet is just they go out on the internet and say I'm looking for a better way to be more effective or I'm looking for a new way to manage a workflow or a project. The other way, and about 40% of our new customers that sign up for licenses come through our collaboration model. And it's actually kind of unique for us at Smartsheet. When you buy a Smartsheet license you are entitled to share your work with an unlimited number of collaborators both inside and outside your company. And those collaborators can view the work but also participate in the process. They can update sheets, they can provide input to the flow, and they actually get to engage and be part of that. And what we find is when people see the power of how other people manage and they participate they realize, hey, I want to use this for some of my work. >> So, it's not read-only? They can actually engage in my project that I've invited them to participate in? >> Absolutely, and the great they for you as the license owner is you never have to worry, hey, I want to share this with Sue. Does she have a license? Or do I have to go let her know ahead of time? You can share freely and not worry about people being able to participate. >> Well you have a big pipeline 'cause I was reading over the weekend that there's about 650,000 active individual users but about 3 million collaborators. >> Absolutely, yeah. >> Is that a differentiator for Smartsheet in terms of enabling that waterfall of demand generation? >> Well, we certainly think it's unique, right? There's lots of folks that kind of go down the free tier path. Where they say I'm going to give you all the capabilities in a free tier but I'm going to try and ratchet it down so you run into these pay walls at every turn where you then have to kind of license the organization wall to wall. And we just feel that doesn't really work for us. For us, we think it's important that anybody that's creating value in Smartsheet should have to pay for the value that they're using it to go create things with. But, the people that they enroll in that process, until they actually are creating their own stuff they should be able to participate for free. We think it really fits nicely with how modern work is evolving with a lot of teams and frankly a lot of teams across organizations. So, interesting fact is almost 40% of sharing in Smartsheet is with people that are outside of the company sharing. So, if you're sharing with all those folks in different companies and working across different organizations, trying to figure out who has what licenses and how to do things is just going to be a nightmare. So we want to make that just completely frictionless. >> So one of the things that is interesting about Smartsheet is that this is designed for the business user. You know, whether you're in sales, IT, finance, engineering, lot of different use cases you talked about kind of the breadth earlier, Jeff. Walk me through, if I'm at a large enterprise organization and I need to launch an omnichannel marketing campaign, but I use email, I use Slack, we got SalesForce, CRM. Maybe some of my, maybe I'm going to be collaborating with a different function that's using a competitive solution. How do you help me, I need to mange this campaign and I need to launch and I need to measure it. How do you help me connect my Slack, my teammates that aren't using it, that are on email. You're smiling big, walk us through that, what does that integration look like? >> Well, I think the first thing to remember is we really focus on the 60% of work that's unstructured and dynamic. So, this is the work that's constantly changing, and many times left to the business user to figure out how to get things done. And we recognize that in managing that unstructured work there are kind of different tools for different parts of the job, right? Just like if you go to any good mechanic's toolbox he's got more than just a hammer in there. And so for the business user they need messaging tools, like Slack or email to communicate. They create new content with document tools, whether it's G-Suite or O365. When they bridge the old world to the new world in the cloud they need file, sync, and share. People still have files, ironically, that sit on desktops and so they need to able to manage those in the cloud. None of those solutions is going to go away. You still need those for different things. Where we play is really helping people manage the what, when, and by whom. How do you actually execute the work? And today there's not a great platform to do that outside of Smartsheet. And so what we try do then is work seamlessly with all the tools that they're using today. So, to answer your question. If you're a Slack user, to get started with integrating Slack with Smartsheet it's as simple as from Slack, turning on the Smartsheet bot. That Smartsheet bot that enables you to receive signal directly from Smartsheet into Slack on update requests, notifications, approval requests that you can then action without ever leaving Slack. You can actually approve an invoice, you can update a Smartsheet directly from that Slack channel. It's the same type of integrations with file, sync, and share where you can attach documents from a Dropbox, or a Onedrive or a Box directly to a row or a sheet. And have a connection to the other work. >> So you're not driving the user back to do everything through the Smartsheet app, I can share things through Slack but I can also be right in Slack having a conversation with a teammate, >> Absolutely. >> And share everything through that directly. >> Absolutely. In fact, our integrations with our messaging platform, which is a core part of our strategy to kind of support how people work today in these modern platforms, really involves two types of integration. There's a channel integration. So, let's so you have a group of folks that are collaborating on some work, you've got a common sheet that you're using to actually manage the details of how you're going to get stuff done and then you have a Slack channel set up for them to engage, communicate, make decisions, collaborate. You can actually send signal directly from that sheet into the channel where everybody sees it in real time. >> What about mobile? >> Totally, works on mobile and on desktop. And then we actually have the bot, intelligent bot based integrations that are more personal. So, that's really your signal and the actions that you need to take. So, we're trying to really cover all bases and how teams want to engage with messaging. >> Just curious, as you've been rolling this out for a couple years, some of the crazier applications that you had no idea that people would use. This application for this type of use case, you can share us a funny, some great stories. >> There's certainly a broad array of use cases and there's a lot of times when you'll hear about a story and then you're like, well, yeah, I guess Smartsheet would be pretty good for that one. But, I would tell you, the ones that to me I get most excited about are the situations where the customer needs to do something really quickly because they're reacting to a signal or something that's happening in the market. And, so, one of the ones that I thought was really cool was actually last year's hurricanes, with Harvey and Irma. One of our large customers, Starbucks, actually used Smartsheet as a tool to connect with their teams and then manage the recovery. And, what they realized going in was they're team members are going to be on the ground in these areas that have been devastated by the hurricane. And they're not going to have internet connectivity, they're probably not going to have power, they got to reach them through mobile. And so they set up a really simple process where every morning they pushed a mobile form in Smartsheet to all of their crew members in those markets. And, the first thing they asked them was, are you okay, do you need help. And then once they knew that they were alright they then said, hey, are you available to work? Your store's going to be re-opening we want to know if you can work? And then with the managers they would poll on what's the state of the physical location and can we actually get open and start serving this community that's been devastated by this disaster? They literally came up with that idea and deployed in a couple of days. And they were getting, if you talk to their CEO, he would tell that he was getting reports every day on the status of who was available and how things were coming together. And then, the funny part of that is, they actually then were able to use that same Smartsheet mobile app to capture all the damage in store and my understanding was it was the first time they were actually able to get full recovery on insurance claims after an event like that. >> Oh, wow, alright. >> That's not really funny but a I really love that. >> Very impactful. >> But it's super important. But so we'll lighten it up a little bit before we let you go. In a prior life, your worked at Coca-Cola. >> I did. >> And you worked on the Freestyle. And for those who aren't familiar, Freestyle is the really cool vending machine when you go to McDonald's or Wendy's or whatever with all the push buttons. And what I find so interesting about Freestyle, it comes up at all the tech conferences. It was really a digital transformation of brown sugar water >> Absolutely. >> Into a phenomenal data stream that provided all types of transformational stories. I wonder if you can tell us a little bit about that story and add a little color 'cause I think it's just a fantastic example of digital transformation of something that probably most people didn't think was possible. >> Sure, oh no, I'd be happy to. It's one of my favorite stories to tell, actually. It was an amazing six year run for me and really what got me into wanting to be in product. I had spent the first half of my career at Coke really in Sales, Marketing and General Management. And, the Freestyle opportunity really came up because our engineering team at Coke had come up with an idea that was really designed to save money on the supply chain. They thought, let's take the water out of all these, the syrup that we ship all over the country and we can make a lot lighter and we'll save all this money. >> All kinds of benefits from not drying the water out. >> And our leadership looked at that and said, hey, we think there might be something more here. And so, the President of the Food Service division at the time invited me to come back to Atlanta and he showed me this prototype and he said, we're not really sure what this is going to be but we want to put a business leader on this to see if there's something here. He said, it can be three months or three years, you never know. So I uprooted my family from Seattle, moved back to Atlanta kind of a little bit on a whim. Like, we could've been over. And the funny thing is he said, there's lots of jobs in Atlanta. What he didn't say was there's lots of jobs at Coke in Atlanta. So, I'm not sure if he was trying to tell me something. But what was really fun about that was that Coke took a completely different approach. For a 125 year old company, completely different approach to innovation than what they traditionally would've done. Which would've been, hey, let's give it to the innovation group. Let's have senior meetings every three months to decide what to do next. >> Kick out new Coke and that didn't work out very well. >> Yeah, well, kick out new Coke. But what they did was they empowered a small team. I basically ran like a little startup. I reported to a board, I had no line reporting and we kept it totally confidential. We isolated the team away from the rest of the organization and we were allowed to just go run. And my board gave us everything we needed as far as resources and money. And we started with the consumer. And we said, hey, what is it that, if we could transform drinking soda away from home what would that look like? And what we found was people wanted a lot more variety than what they were getting at that time. Used to be six choices, right? Five of them brown, one of them caffeine-free, one of them diet, no caffeine-free diet, I mean it was just like we were missing the boat, right? If you went to a 7-11 there's 3,000 choices. You go to McDonald's, six. And so they wanted a lot more choice and they wanted to be able to pour the drink themselves. And so we thought that was a really powerful insight. What was interesting about that is they didn't trust the kid behind the counter to get it right. Which, I think is kind of ironic but at the end of the day, we invented the technology around this idea of providing almost unlimited choice and really rolling the consumer in the process of creation and it was amazing. When we delivered that to our end users we saw 50% increases in volume, remember this is a brand that's growing 2%, 3%. >> Super mature market. >> Yeah, super mature market, complete game changer. And it really unleashed this sense of creativity with consumers around what's possible, right, on the drink side. Now, on the business side, what was transformative for the company was that this was a completely wired experience. And it had had to be, frankly, to pull off delivering a 125 different choices in a restaurant. You got to arm that customer with lots of information about what do they need, how to configure, how to service. And so those machines were all connected. And they provided tons of great data on what consumption was happening inside the restaurant. But, also air logs on the equipment, how everything was performing and so it really led to a completely rethinking. How do you actually manage a network of connected devices? And it was kind of funny because we were really kind of pre-cloud. >> Pre-cloud, pre IoT. >> When we first started we had the machines called home every night using a wireless modem. We actually started, we did a contract with a IT service provider to provide servers to actually house the data. And we did this contract, oh these will hold you for three years. Within a month we were running out of server capacity because all of the data we were getting. And so it was really, it was super, super fun and we iterated that I spent six years on that and really it was one of the coolest experiences of my life. >> Probably one of the most relatable digital transformation stories and you guys are also doing that at Smartsheet. Gene, I wish we had more time. Thank you so much for stopping by the Cube and one of the things I like that you said in the beginning, and we'll leave it with this, is that Smartsheet is authentic. I think authenticity is contagious. So, thank you for your time. >> Well, thanks for having me, it's a pleasure. >> Thanks. >> And for Jeff Frick, I'm Lisa Martin. You're watching the Cube live from Smartsheet ENGAGE in Bellevue, Washington. Stick around, Jeff, and I'll be right back with our next guest. (upbeat music)

(exciting electronic music) >> From San Francisco, it's theCUBE, covering Conga Connect West 2018. Brought to you by Conga. >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're in downtown San Francisco at the Thirsty Bear. We're at Dreamforce. I can't get an official number, I keep asking, but the number they're throwing around is 170,000 people, so if you're coming, do not bring your car. It will take you four days to get here from AT&T and I think the Giants have a home game today, too, which just makes things even more interesting. But we're at a special side event, it's the Conga Connect West event here at the Thirsty Bear, three doors down from Moscone South, so we're excited to be here. It's our first time at Salesforce, and to kick things off, we've got Bob DeSantis, the chief operating officer of Conga, and with him, Jason Gabbard, the head of AI strategy. So gentlemen, welcome. >> Thank you. >> Good morning, great to be here with you. >> So what a cool event. You guys have this thing rented out for three days. >> Yep. You've got entertainment, you've got the silent disco. I think tomorrow night, some crazy bands. >> Yeah, we've got an open bar, food going all day and all night, actually we did this last year, and we were so crowded that this year we rented the parking lot behind and we built two circus tents so we actually extend all the way out to the next block. We have multiple sponsors here helping us to bring their customers and their partners in. So, open bar, open food, meeting rooms, demo stations, a place to come and relax and kick back a little bit from the chaos of those 170,000 people just a block away. >> It's just crazy, so come on down and meet the Conga crew and all the people, you have a good time. Let's jump into it. The topic at hand is AI. We are all the buzz about AI, AI, AI, machine learning, artificial intelligence, and what we hear time and time again is no one, I just need to go buy some AI. Really that's not the way the implementation is going to work, but where we see it in a great example I like to use a lot that people are familiar with is Gmail, those little tiny automated responses back to that email, there's actually a ton of AI behind those setting context and voice, and this that and the other. How are you guys leveraging AI in your solutions? You've been at this for a while. AI represents a great new opportunity. >> Yeah, it really is, Jason do you want to? >> Yeah, sure, you may not be aware, but Conga has actually been developing AI inside of the contract management system for a few years now, and I came over to Conga in connection with the acquisition of a company I founded focused on AI, and so obviously, things are getting a lot more interesting, technology is getting a lot more robust. You know, I think you made a great analogy to Gmail. Inside of the Conga CLM, Conga Contracts, you'll actually see that we're starting to make suggestions around contracts, so you may load a document in and you might see a popup over in the margin that says, "Hey, is this a limitation of liability clause?" So that's one example of AI working in the background of CLM. >> Well, I was going to say, what are some of the things you look for? I had a friend years ago, he had a contract management company, and I was like, "How?" And this was before OCR, and it was not good. "How? How are you doing this?" He goes, "No, if we just tell them where's the document and when does it expire, huge value there." He sold the company, he made a ton of money. But obviously, time has moved along. A lot of different opportunities now, so what are some of the things you do in contract lifecycle management? >> Think of that example as phase one of contract lifecycle management. Just get all my contracts into a common repository, give me some key metadata, like what's the value, who are the counterparties, and what's the expiration date? That's huge. So, ten years ago, 15 years ago, that was the cutting edge of CLM, contract lifecycle management, now the evolution has continued, we're in what we think of as sort of the third phase of CLM. So now, how do we actually pull actionable data out of contracts? So having the contract, you mentioned OCR, having machine readable data in a repository is great, but what's actually in the contract? What did we negotiate six months ago that now could have an impact on our business if we knew it? If we could act on it? And so with Conga AI, and the machine learning technology that Jason's company developed, and that we've now embedded in our CLM products, we can unlock the data that's hidden in documents, and make it actionable for our customers. >> So one of the things that you used to trigger that action, because the other thing about contracts we always think about, right, is you negotiate them, it's a pain in the butt, you sign them, then you put them in the file cabinet, nobody thinks about it again. So in terms of making that more of a living document beyond it's just simply time to renew, what are some of the things that you look for using the AI? Are you flagging bad things, are you looking for good things, are you seeing deltas? What are you looking for? >> I'll give you a really concrete example. We recently had a customer that negotiated a payment term to their benefit with one of their suppliers, but that payment term was embedded in the document, and their payables team was paying on net 30 when their negotiators had negotiated net 90. That data was locked in the contract. With Conga AI, we can pull that data out, update the system of record, in that case, it would have been SAP, and now the payables team can take advantage of those hard fought wins in that contract negotiation. That's just one example. >> Yeah, so two obvious use cases we're seeing day in and day out right now, number one, I'll call an on ramp to the CLM, so that's likely a new customer or relatively new customer at Conga that says, "Hey, I have 50,000 contracts." I was on the phone this morning with this precise use case. "I have 50,000 contracts, really happy to be part of the Conga family, get my CLM up and running, but now I got to get those 50,000 contracts into the system, so how do we do that?" Well, there's one way to do that, get a bunch of people together and work for a couple years and we'll have it done. The other way is to use AI to accelerate some of that. Classic misconception is that the AI is going to do all of the work, that's just not the case. At Conga, we tend to take more of a human computer symbiosis sort of working side by side, and the AI can really do the first pass. You might be able to automate something like 75% of the fields, so you can take your reduced team of people then and get the rest of the information into the system and verified, but we may be able to cut that down from a couple years to 30, 60 days, something like that, so that's one obvious use case for the technology, and then I think the second is more of a stare and compare exercise. Historically, you would see companies come in and say, "If I'm going to sign an NDA, it's got to have the following ten features, and I'll never accept x, y, and z." So we can sort of key to that with our AI, and take the first pass of a document and really do the triage, and so again, while it may not be 100%, we'll get to 80-90% and say, "Here are the three or four areas where you need to let your knowledge workers focus." >> And are there some really discrete data points that you call out in a defined field for every single contract because there always are payment terms, I imagine, obviously dates and signatures, so some of those things that are pretty consistent across the board versus, I would imagine, all of the crazy, esoteric-y stuff, which is probably their corner cases that people focus too much on relative to the value that you can get across that entire pop, 50,000 contracts is a lot of contracts. >> I don't know what your view is, but for me, I think it's follow the money. Everyone always cares about dollars, when I'm getting my dollars, and the other is follow very high risk stuff. Like indemnities, limitations and liability, occasionally you're seeing people interested in change in control, what happens if I sell my company or take on a bunch of financing, does that trigger anything? >> What's interesting about contracts is there are hundreds if not thousands of different potential clauses that could live in a contract, but in general, sort of the 90-10 rule is that there's about 40 clauses that you find in most commercial agreements, most business to business, or even business to consumer commercial agreements, so with Conga Machine Learning, we train based on the sort of use cases that extend that for a specific domain. So for example, we've done a lot of work in commercial real estate, right? So those commercial real estate agreements have that core base, but then they have unique attributes that are unique to commercial real estate, so Conga Machine Learning, as part of the Conga AI suite, can be trained to learn so that we can reduce that cycle time. You know, when we go into our tenth commercial real estate use case, it's going to be a lot more efficient, a lot faster, and a lot higher initial hit than we start training it at the beginning. For us, it's about helping customers consume the documents that make sense for their business. And machine learning is intuitively about learning, so there is this process that has to take place, but it's amazing how quickly it can learn. You use the google example, I like to think of the Amazon.com suggestion service example. They literally know what I'm going to buy before I'm going to buy it. >> Right, right. >> That didn't just happen yesterday, they've been learning that from me for the last 20 years or 15 years. We're at sort of the beginning of that phase right now in terms of B to B CLM, but it's amazing how quickly it's moving, and how quickly it's having an impact on our customers businesses. >> Yeah, I was going to ask, so where are we on the lifecycle of the opportunity of using AI in these contracts beyond just the signature date and the renewal date for some of these things? And also I would imagine, you guys can tie some of that back into your document creation process >> That's right. >> So that you again remove a lot of anomalies, and get more of a standardized process >> Yeah, so Conga provides a full digital document transformation suite, and that includes, as you mentioned, document generation capabilities, contract management, Conga AI >> Signature, the whole thing, right? >> Conga sign. So we're not here yet, but imagine if through Conga AI, we're able to learn what type of clause structure actually has a higher close rate, or a faster cycle time, or a higher dollar value for a given book of business, so customer x is selling their products to consumers or other businesses, and if we can learn, we can, how their contracts streamline and improve their effectiveness, then we can feed that right back into the creation side of their business. So that's just over the horizon. >> And then the other thing, I would imagine, is that you can get the best practices both inter-department, inter-company, and then I don't know where the legal limits are in terms of using it anonymized and the best practice data to publish benchmarks and stuff, which we're seeing more and more because people want to know the benefits of using so many of these things. You know, what's next? And then do you see triggers? Will some day it will be a trigger mechanism or is it really more a kind of an audit and adjust going forward? >> From my perspective, I think the some day is more, we're extremely focused on the analytics and the kind of discovery of documents right now, but I think looking out over the one year horizon, it's less about triggers and more about more touchpoints in the work close, and so really optimizing the contracting process, so being able to walk into a company and say, "Hey, I know you would like for this to be in all your contracts, but as a matter of practice, it's not, so maybe we need to abandon that policy, and get to a signed document faster. So more of that type of exercise with AI, and also integrating with sibling systems and testing what you expected to happen in the document versus what actually happened. That may be vis-à-vis an integration with ERP or something like that. >> It's pretty amazing, because as we know, the stuff learns fast. >> It does. >> From watching that happen with the chess and the go and everything else, and you read some of the books about exponential curves, you'll get down that path probably faster than we think. >> Yes. >> Well, Bob, Jason, thanks for taking a few minutes, and again thanks for inviting us to this cool event, and everybody come on down, there's lots of free food and drinks. >> Come down to the Thirsty Bear. >> Thanks so much. >> Alright, he's Bob, he's Jason, I'm Jeff, you're watching theCUBE. We're at the Conga Connect West event at Dreamforce at the Thirsty Bear, come on down and see us. Thanks for watching. (energetic electronic music)

>> Live from San Jose in the heart of Silicon Valley, it's theCUBE, covering DataWorks Summit 2018, brought to you by Hortonworks. >> Welcome back to theCUBE's live coverage of DataWorks here in San Jose, California. I'm your host, Rebecca Knight, along with my co-host, James Kobielus. We have with us Eric Herzog. He is the Chief Marketing Officer and VP of Global Channels at the IBM Storage Division. Thanks so much for coming on theCUBE once again, Eric. >> Well, thank you. We always love to be on theCUBE and talk to all of theCUBE analysts about various topics, data, storage, multi-cloud, all the works. >> And before the cameras were rolling, we were talking about how you might be the biggest CUBE alum in the sense of you've been on theCUBE more times than anyone else. >> I know I'm in the top five, but I may be number one, I have to check with Dave Vellante and crew and see. >> Exactly and often wearing a Hawaiian shirt. >> Yes. >> Yes, I was on theCUBE last week from CISCO Live. I was not wearing a Hawaiian shirt. And Stu and John gave me a hard time about why was not I wearing a Hawaiian shirt? So I make sure I showed up to the DataWorks show- >> Stu, Dave, get a load. >> You're in California with a tan, so it fits, it's good. >> So we were talking a little bit before the cameras were rolling and you were saying one of the points that is sort of central to your professional life is it's not just about the storage, it's about the data. So riff on that a little bit. >> Sure, so at IBM we believe everything is data driven and in fact we would argue that data is more valuable than oil or diamonds or plutonium or platinum or silver to anything else. It is the most viable asset, whether you be a global Fortune 500, whether you be a midsize company or whether you be Herzogs Bar and Grill. So data is what you use with your suppliers, with your customers, with your partners. Literally everything around your company is really built around the data so most effectively managing it and make sure, A, it's always performant because when it's not performant they go away. As you probably know, Google did a survey that one, two, after one, two they go off your website, they click somewhere else so has to be performant. Obviously in today's 365, 7 by 24 company it needs to always be resilient and reliable and it always needs to be available, otherwise if the storage goes down, guess what? Your AI doesn't work, your Cloud doesn't work, whatever workload, if you're more traditional, your Oracle, Sequel, you know SAP, none of those workloads work if you don't have a solid storage foundation underneath your data driven enterprise. >> So with that ethos in mind, talk about the products that you are launching, that you newly launched and also your product roadmap going forward. >> Sure, so for us everything really is that storage is this critical foundation for the data driven, multi Cloud enterprise. And as I've said before on theCube, all of our storage software's now Cloud-ified so if you need to automatically tier out to IBM Cloud or Amazon or Azure, we automatically will move the data placement around from one premise out to a Cloud and for certain customers who may be multi Cloud, in this case using multiple private Cloud providers, which happens due to either legal reasons or procurement reasons or geographic reasons for the larger enterprises, we can handle that as well. That's part of it, second thing is we just announced earlier today an artificial intelligence, an AI reference architecture, that incorporates a full stack from the very bottom, both servers and storage, all the way up through the top layer, then the applications on top, so we just launched that today. >> AI for storage management or AI for run a range of applications? >> Regular AI, artificial intelligence from an application perspective. So we announced that reference architecture today. Basically think of the reference architecture as your recipe, your blueprint, of how to put it all together. Some of the components are from IBM, such as Spectrum Scale and Spectrum Computing from my division, our servers from our Cloud division. Some are opensource, Tensor, Caffe, things like that. Basic gives you what the stack needs to be, and what you need to do in various AI workloads, applications and use cases. >> I believe you have distributed deep learning as an IBM capability, that's part of that stack, is that correct? >> That is part of the stack, it's like in the middle of the stack. >> Is it, correct me if I'm wrong, that's containerization of AI functionality? >> Right. >> For distributed deployment? >> Right. >> In an orchestrated Kubernetes fabric, is that correct? >> Yeah, so when you look at it from an IBM perspective, while we clearly support the virtualized world, the VM wares, the hyper V's, the KVMs and the OVMs, and we will continue to do that, we're also heavily invested in the container environment. For example, one of our other divisions, the IBM Cloud Private division, has announced a solution that's all about private Clouds, you can either get it hosted at IBM or literally buy our stack- >> Rob Thomas in fact demoed it this morning, here. >> Right, exactly. And you could create- >> At DataWorks. >> Private Cloud initiative, and there are companies that, whether it be for security purposes or whether it be for legal reasons or other reasons, don't want to use public Cloud providers, be it IBM, Amazon, Azure, Google or any of the big public Cloud providers, they want a private Cloud and IBM either A, will host it or B, with IBM Cloud Private. All of that infrastructure is built around a containerized environment. We support the older world, the virtualized world, and the newer world, the container world. In fact, our storage, allows you to have persistent storage in a container's environment, Dockers and Kubernetes, and that works on all of our block storage and that's a freebie, by the way, we don't charge for that. >> You've worked in the data storage industry for a long time, can you talk a little bit about how the marketing message has changed and evolved since you first began in this industry and in terms of what customers want to hear and what assuages their fears? >> Sure, so nobody cares about speeds and feeds, okay? Except me, because I've been doing storage for 32 years. >> And him, he might care. (laughs) >> But when you look at it, the decision makers today, the CIOs, in 32 years, including seven start ups, IBM and EMC, I've never, ever, ever, met a CIO who used to be a storage guy, ever. So, they don't care. They know that they need storage and the other infrastructure, including servers and networking, but think about it, when the app is slow, who do they blame? Usually they blame the storage guy first, secondarily they blame the server guy, thirdly they blame the networking guy. They never look to see that their code stack is improperly done. Really what you have to do is talk applications, workloads and use cases which is what the AI reference architecture does. What my team does in non AI workloads, it's all about, again, data driven, multi Cloud infrastructure. They want to know how you're going to make a new workload fast AI. How you're going to make their Cloud resilient whether it's private or hybrid. In fact, IBM storage sells a ton of technology to large public Cloud providers that do not have the initials IBM. We sell gobs of storage to other public Cloud providers, both big, medium and small. It's really all about the applications, workloads and use cases, and that's what gets people excited. You basically need a position, just like I talked about with the AI foundations, storage is the critical foundation. We happen to be, knocking on wood, let's hope there's no earthquake, since I've lived here my whole life, and I've been in earthquakes, I was in the '89 quake. Literally fell down a bunch of stairs in the '89 quake. If there's an earthquake as great as IBM storage is, or any other storage or servers, it's crushed. Boom, you're done! Okay, well you need to make sure that your infrastructure, really your data, is covered by the right infrastructure and that it's always resilient, it's always performing and is always available. And that's what IBM drives is about, that's the message, not about how many gigabytes per second in bandwidth or what's the- Not that we can't spew that stuff when we talk to the right person but in general people don't care about it. What they want to know is, "Oh that SAP workload took 30 hours and now it takes 30 minutes?" We have public references that will say that. "Oh, you mean I can use eight to ten times less storage for the same money?" Yes, and we have public references that will say that. So that's what it's really about, so storage is really more from really a speeds and feeds Nuremberger sort of thing, and now all the Nurembergers are doing AI and Caffe and TensorFlow and all of that, they're all hackers, right? It used to be storage guys who used to do that and to a lesser extent server guys and definitely networking guys. That's all shifted to the software side so you got to talk the languages. What can we do with Hortonworks? By the way we were named in Q1 of 2018 as the Hortonworks infrastructure partner of the year. We work with Hortonworks all time, at all levels, whether it be with our channel partners, whether it be with our direct end users, however the customer wants to consume, we work with Hortonworks very closely and other providers as well in that big data analytics and the AI infrastructure world, that's what we do. >> So the containerizations side of the IBM AI stack, then the containerization capabilities in Hortonworks Data Platform 3.0, can you give us a sense for how you plan to, or do you plan at IBM, to work with Hortonworks to bring these capabilities, your reference architecture, into more, or bring their environment for that matter, into more of an alignment with what you're offering? >> So we haven't an exact decision of how we're going to do it, but we interface with Hortonworks on a continual basis. >> Yeah. >> We're working to figure out what's the right solution, whether that be an integrated solution of some type, whether that be something that we do through an adjunct to our reference architecture or some reference architecture that they have but we always make sure, again, we are their partner of the year for infrastructure named in Q1, and that's because we work very tightly with Hortonworks and make sure that what we do ties out with them, hits the right applications, workloads and use cases, the big data world, the analytic world and the AI world so that we're tied off, you know, together to make sure that we deliver the right solutions to the end user because that's what matters most is what gets the end users fired up, not what gets Hortonworks or IBM fired up, it's what gets the end users fired up. >> When you're trying to get into the head space of the CIO, and get your message out there, I mean what is it, what would you say is it that keeps them up at night? What are their biggest pain points and then how do you come in and solve them? >> I'd say the number one pain point for most CIOs is application delivery, okay? Whether that be to the line of business, put it this way, let's take an old workload, okay? Let's take that SAP example, that CIO was under pressure because they were trying, in this case it was a giant retailer who was shipping stuff every night, all over the world. Well guess what? The green undershirts in the wrong size, went to Paducah, Kentucky and then one of the other stores, in Singapore, which needed those green shirts, they ended up with shoes and the reason is, they couldn't run that SAP workload in a couple hours. Now they run it in 30 minutes. It used to take 30 hours. So since they're shipping every night, you're basically missing a cycle, essentially and you're not delivering the right thing from a retail infrastructure perspective to each of their nodes, if you will, to their retail locations. So they care about what do they need to do to deliver to the business the right applications, workloads and use cases on the right timeframe and they can't go down, people get fired for that at the CIO level, right? If something goes down, the CIO is gone and obviously for certain companies that are more in the modern mode, okay? People who are delivering stuff and their primary transactional vehicle is the internet, not retail, not through partners, not through people like IBM, but their primary transactional vehicle is a website, if that website is not resilient, performant and always reliable, then guess what? They are shut down and they're not selling anything to anybody, which is to true if you're Nordstroms, right? Someone can always go into the store and buy something, right, and figure it out? Almost all old retailers have not only a connection to core but they literally have a server and storage in every retail location so if the core goes down, guess what, they can transact. In the era of the internet, you don't do that anymore. Right? If you're shipping only on the internet, you're shipping on the internet so whether it be a new workload, okay? An old workload if you're doing the whole IOT thing. For example, I know a company that I was working with, it's a giant, private mining company. They have those giant, like three story dump trucks you see on the Discovery Channel. Those things cost them a hundred million dollars, so they have five thousand sensors on every dump truck. It's a fricking dump truck but guess what, they got five thousand sensors on there so they can monitor and make sure they take proactive action because if that goes down, whether these be diamond mines or these be Uranium mines or whatever it is, it costs them hundreds of millions of dollars to have a thing go down. That's, if you will, trying to take it out of the traditional, high tech area, which we all talk about, whether it be Apple or Google, or IBM, okay great, now let's put it to some other workload. In this case, this is the use of IOT, in a big data analytics environment with AI based infrastructure, to manage dump trucks. >> I think you're talking about what's called, "digital twins" in a networked environment for materials management, supply chain management and so forth. Are those requirements growing in terms of industrial IOT requirements of that sort and how does that effect the amount of data that needs to be stored, the sophistication of the AI and the stream competing that needs to be provisioned? Can you talk to that? >> The amount of data is growing exponentially. It's growing at yottabytes and zettabytes a year now, not at just exabytes anymore. In fact, everybody on their iPhone or their laptop, I've got a 10GB phone, okay? My laptop, which happens to be a Power Book, is two terabytes of flash, on a laptop. So just imagine how much data's being generated if you're doing in a giant factory, whether you be in the warehouse space, whether you be in healthcare, whether you be in government, whether you be in the financial sector and now all those additional regulations, such as GDPR in Europe and other regulations across the world about what you have to do with your healthcare data, what you have to do with your finance data, the amount of data being stored. And then on top of it, quite honestly, from an AI big data analytics perspective, the more data you have, the more valuable it is, the more you can mine it or the more oil, it's as if the world was just oil, forget the pollution side, let's assume oil didn't cause pollution. Okay, great, then guess what? You would be using oil everywhere and you wouldn't be using solar, you'd be using oil and by the way you need more and more and more, and how much oil you have and how you control that would be the power. That right now is the power of data and if anything it's getting more and more and more. So again, you always have to be able to be resilient with that data, you always have to interact with things, like we do with Hortonworks or other application workloads. Our AI reference architecture is another perfect example of the things you need to do to provide, you know, at the base infrastructure, the right foundation. If you have the wrong foundation to a building, it falls over. Whether it be your house, a hotel, this convention center, if it had the wrong foundation, it falls over. >> Actually to follow the oil analogy just a little bit further, the more of this data you have, the more PII there is and it usually, and the more the workloads need to scale up, especially for things like data masking. >> Right. >> When you have compliance requirements like GDPR, so you want to process the data but you need to mask it first, therefore you need clusters that conceivably are optimized for high volume, highly scalable masking in real time, to drive the downstream app, to feed the downstream applications and to feed the data scientist, you know, data lakes, whatever, and so forth and so on? >> That's why you need things like Incredible Compute which IBM offers with the Power Platform. And why you need storage that, again, can scale up. >> Yeah. >> Can get as big as you need it to be, for example in our reference architecture, we use both what we call Spectrum Scale, which is a big data analytics workload performance engine, it has multiple threaded, multi tasking. In fact one of the largest banks in the world, if you happen to bank with them, your credit card fraud is being done on our stuff, okay? But at the same time we have what's called IBM Cloud Object Storage which is an object store, you want to take every one of those searches for fraud and when they find out that no one stole my MasterCard or the Visa, you still want to put it in there because then you mine it later and see patterns of how people are trying to steal stuff because it's all being done digitally anyway. You want to be able to do that. So you A, want to handle it very quickly and resiliently but then you want to be able to mine it later, as you said, mining the data. >> Or do high value anomaly detection in the moment to be able to tag the more anomalous data that you can then sift through later or maybe in the moment for realtime litigation. >> Well that's highly compute intensive, it's AI intensive and it's highly storage intensive on a performance side and then what happens is you store it all for, lets say, further analysis so you can tell people, "When you get your Am Ex card, do this and they won't steal it." Well the only way to do that, is you use AI on this ocean of data, where you're analyzing all this fraud that has happened, to look at patterns and then you tell me, as a consumer, what to do. Whether it be in the financial business, in this case the credit card business, healthcare, government, manufacturing. One of our resellers actually developed an AI based tool that can scan boxes and cans for faults on an assembly line and actually have sold it to a beer company and to a soda company that instead of people looking at the cans, like you see on the Food Channel, to pull it off, guess what? It's all automatically done. There's no people pulling the can off, "Oh, that can is damaged" and they're looking at it and by the way, sometimes they slip through. Now, using cameras and this AI based infrastructure from IBM, with our storage underneath the hood, they're able to do this. >> Great. Well Eric thank you so much for coming on theCUBE. It's always been a lot of fun talking to you. >> Great, well thank you very much. We love being on theCUBE and appreciate it and hope everyone enjoys the DataWorks conference. >> We will have more from DataWorks just after this. (techno beat music)

(techno music) [Announcer] Live from Las Vegas, its the Cube! Covering Fortinet Accelerate 18. Brought to you buy Fortinet. >> Welcome back to the Cube's continuing coverage of Fortinet Accelerate 2018. I'm Lisa Martin joined by my cohost Peter Burris, and we have a very cozy set. Right now, I'd like to introduce you to our next guests, Daniel Bernard, the vice-president of business development for SentinelOne, and Basil Habib, you are the IT director at Tri City Foods. Gentleman, welcome to the Cube. >> Great to be here, thanks. >> We're excited to have you guys here. So first, Daniel first question to you. Tell us about SentinelOne, what's your role there, and how does SentinelOne partner with Fortinet? >> Sure, I run technologies integration and alliances. SentinelOne is a next generation endpoint protection platform company. Where we converge EPP and EDR into one agent that operates autonomously. So whether its connected to the internet or not, we don't rely on a cloud deliver solution. It works just as well online and offline. And we're there to disrupt the legacy AV players that have been in this market for 25 years with technology driven by artificial intelligence to map every part of the threat life cycle to specific AI capabilities, so we can stop attacks before they even occur. >> And your partnership with Fortinet, this is your first Accelerate, so talk to us about the duration of that partnership and what is differentiating-- >> Yeah. >> Lisa: For you. >> Its great to be here at Accelerate and also to work with Fortinet. We've been working with them for about a year and a half, and we're proud members of the Fortinet Security Fabric. What it means to us is that for enterprises, like Tri City Foods that we'll talk about, a defense and depth approach is really the way to go. Fortinet, leading edge, network security solutions. We have a very meaningful and exciting opportunity to work with Fortinet, given the breadth of our APIs. We have over 250 APIs, the most of any endpoint solution out there on the market. So the things we can enable within Fortinet's broad stack is really powerful. Fortinet has a lot of customers, a lot of endpoints in their environments to protect. So we're proud to partner with Fortinet to help go after those accounts together. To not only go into those accounts ourselves but also strengthen the security that Fortinet is able to offer their customers as well. >> If we can pivot on that for just a second. How do you-- how does SentinelOne help strengthen, for example, some of the announcements that came out from Fortinet this morning about the Security Fabric? How do you give an advantage to Fortinet? >> Sure. So where we come in, is we sit at the endpoint level and we're able to bring a lot of different pieces of intelligence to core and critical Fortinet assets. For example, with the Fortinet connector that we are going to be releasing tomorrow, so a little sneak peek on that right here on the Cube. The endpoint intelligence is actually through API to API connections able to go immediately into FortiSandbox and then be pushed to FortiGate. And that's in real time. So, whether an endpoint is inside of a network or running around somewhere in the world, whether its online or offline, a detection and a conviction we make through the SentinelOne client and the agent that actually sits on the endpoint, all the sudden is able to enrich and make every single endpoint inside of a Fortinet network much smarter and prone and also immune from attacks before they even occur. >> So as you think about that, how does it translate into a company like Tri City which has a large number of franchises, typically without a lot of expertise in those franchises, to do complex IT security but still very crucial data that has to be maintained and propagated. >> Well from Tri City's perspective, we look into security environment. And when you look into the Security Fabric between Fortinet and SentinelOne, that really helps us out a great deal. By looking into automating some of theses processes, mitigating some of these threats, that integration and the zero-day attack that can be prevented, that really helps us out day one. >> So tell us a little bit about Tri City. >> Well Tri City Foods is basically the second largest Franchisees for Burger King. We currently have approximately about 500 locations. Everybody thinks about Burger King as just the, you know, you go purchase Whopper. But nobody knows about all of the technology that goes in the back and in order to support that environment. You look into it, you got the Point of Sale, taking your credit card transaction, you got your digital menu board, you got all of the items in the back end, the drive-through. And we support all of those devices and we ensure that all of these are working properly, and operating efficiently. So if one of these devices is not functioning, that's all goes down. The other thing we do is basically we need to ensure that the security is up, most important for us. We're processing credit card transaction, we cannot afford to have any kind of issue to the environment. And this is, again, this is were SentinelOne comes into the picture where all of our devices down there are protected with the solution, as well as protecting the assets with Fortinet security. >> So I hear big environment complexity. Tell us about the evolution of security in your environment. You mention SentinelOne but how has that evolved as you have to, you said so many different endpoints that are vulnerable and there's personal information. Tell us about this evolution that you helped drive. >> The issue I put an end to when I first started on that is, we had the traditional antivirus. We had traditional antivirus, its just basically protecting what it knows about, it did not protect anything that is zero-day. We got in a head to a couple ransom wares. Which we are not willing to take any chances with the environment. That evolution came through as, no we cannot afford to have these type of system be taken down or be compromised. And we do like to assure the security of our clients. So this is, again, this is where we decided to go into the next gen and for protection. Ensuring the uptime and the security of the environment. >> But very importantly, you also don't have the opportunity to hire really, really expensive talent in the store to make sure that the store is digitally secure. Talk a little bit about what Daniel was talking about, relative to AI, automation, and some of the other features that you're looking for as you ensure security in those locations. >> The process to go down there is basically, we cannot expect everybody to understand security. So in order-- >> That's a good bet! (laughing) >> So in order to make-- >> While we're all here! >> That's right! >> So in order to make it easy for everybody to process the solutions, its best if we have to simplify as much as possible. We need to make sure its zero touch, we need to make sure that it works all the time, irrelevant to if you are on the network or off the network. We needed to make sure that its reliable and it works without any compromise. >> And very importantly, its multibonal right? It can be online, offline, you can have a variety of different operator characteristics, centralized, more regional. Is that all accurate? >> Multi-tenant, on-prem. >> Definitely. With every location, you got your local users, you have your managers, the district managers, they are mobile. These are mobile users that we have to protect. And in order to protect them we need to make sure that they are protected offline as well as online. And again, the SentinelOne client basically provided that security for us. It is always on, its available offline, and its preventing a lot of malware from coming in. >> Talk to us about, kind of the reduction in complexity and visibility. Cause I'm hearing that visibility is probably a key capability that you now have achieved across a pretty big environment. >> Correct. So, before with the traditional antivirus, you got on-prem solution. On-prem solution, in order to see that visibility, you have be logged in, you have to be able to access that solution, you have to be pushing application updates, signature updates, its very static. Moving into SentinelOne, its a successful solution. I don't have to touch anything, basically everything works in the background. We update the backend and just the clients get pushed, the updates get pushed, and its protected. I only have one engineer basically looking after the solution. Which is great in this environment. Because again, everywhere you go, up access is a big problem. So in order to reduce the cost, we need to make sure that we have that automation in place. We need to make sure that everything works with minimal intervention. That issues were mitigated dynamically without having any physical intervention to it. And this where the solution came in handy. >> So I'm hearing some really strong positive business outcomes. If we can kind of shift, Daniel, back to you. This is a great testimonial for how a business is continuing to evolve and grow at the speed and scale that consumers are demanding. Tell us a little bit on the SentinelOne side about some of the announcements that Fortinet has made today. For example, the Security Fabric, as well as what they announced with AI. How is that going to help your partnership and help companies like Tri City Foods and others achieve the visibility and the security that they need, at that scale and speed that they demand. >> Yeah I think Fortinet has very progressive approach when it comes to every part of their stack. What we see with the Fortinet Security Fabric is a real desire to work with best of breed vendors and bring in their capabilities so that customers can still utilize all the different pieces of what Fortinet offers, whether it be FortiGate, FortiSandbox, FortiMail, all these different fantastic products but compliment those products and enrich them with all these other great vendors here on the floor. And what we heard from Basil is what we hear from our other 2000 customers, these themes of we need something that's simple. With two people on the team, you can easily spend all your time just logging into every single console. Fortinet brings that light so seamlessly in their stack 20, 30 products that are able to be easily managed. But if you don't partner with a vendor like Fortinet or SentinelOne and your going into all these different products all day long, there's no time to actually do anything with that data. I think the problem in cyber security today is really one of data overload. What do you do with all this data? You need something that's going to be autonomous and work online and offline but also bring in this level of automation to connect all these different pieces of a security ecosystem together to make what Fortinet has very nicely labeled a Security Fabric. And that's what I believe is what's going inside Basil's environment, that's what we see in our 2000 customers and hopefully that's something that all of Fortinet's customers can benefit from. >> Basil, one of the many things that people think about is they associate digital transformation with larger businesses. Now, Tri City Food is not a small business, 500 Burger King franchises is a pretty sizable business, when you come right down to it. But how is SentinelOne, Fortinet facilitating changes in the in-store experience? Digital changes in the in-store experience? Are there things that you can now think about doing as a consequence of bringing this endpoint security into the store, in an automated, facile, simple way that you couldn't think about before? >> Actually yes, by using the Fortinet platform we deployed the FortiAPs. We have the FortiManager, we're looking into, basically, trying to manage and push all of the guest services, to provide guest services. Before we had to touch a lot of different devices, right now its just two click of a button and I'm able to provide that SSID to all of my stores. We're able to change the security settings with basically couple clicks. We don't have to go and manage 500 locations. I'm only managing a single platform and FortiManager, for instance, or FortiCloud. So this is very progressive for us. Again, when you're working with a small staff, the more automation and the more management you can do on the backend to simplify the environment, as well as providing the required security is a big plus for us. >> There's some key features that we've brought to market to help teams like Basil's. A couple ones that come to mind, our deep visibility capability where you can actually see into encrypted traffic directly from the endpoint, without any changes in network topography. That's something that's pretty groundbreaking. We're the only endpoint technology to actually do that, where you can actually threat hunt for IOCs and look around and see 70 percent of traffics encrypted today and that number is rising. You can actually see into all that traffic and look for specific data points. That's a really good example, where you can turn what you use to have to go to a very high level of SOC analyst and you can have anybody actually benefit from a tool like that. The other one that comes to mind is our rollback capability, where if something does get through or we're just operating in EDR mode, by customer choice, you can actually completely rollback a system to the previously noninfected, nonencrypted state directly from that central location. So whether that person is on an island or in Bermuda, or sitting in a store somewhere, if a system is compromise you don't need to re-image it anymore. You can just click rollback and within 90 seconds its back to where it was before. So, the time savings we can drive is really the key value proposition from a business outcome standpoint because you need all these different check boxes and more than check boxes, but frankly there's just not the people and the hours in the day to do it all. >> So, you said time savings affects maybe resource allocation. I'm wondering in terms of leveraging what you've established from a security standpoint as differentiation as Tri City is looking to grow and expand. Tell us a little bit about how this is a differentiator for your business, compared to your competition. >> I cannot speak to the competition. (all laughs) What I can speak to is, again, the differentiator for us as Daniel mentioned is basically, again, the automation pieces, the rollback features. The minimizing the threat analyses into the environment. All these features basically is going to make us more available for our customers, the environment is going to be secure and customers will be more than welcome to come into us and they know that their coming in their information is secure and their not going to be compromised. >> Well are you able to set up stores faster? Are you able to, as you've said, roll out changes faster? So you do get that common kind of view of things. >> We're at zero zero breach. >> We're at zero zero breach yes. So, basically, in order through a lot faster, we do it lock the source faster. We basically, with the zero touch deployment, that Fortinet is offering, basically send the device to the store, bring it online and its functional. We just push it out the door and its operational. With the SentinelOne platform, push the client to the store and set it and forget it. That is basically the best solution that we ever deployed. >> Set it and forget it. >> I like that. >> Set it and forget it. >> That's why you look so relaxed. (laughs) >> I can sleep at night. (all laugh) >> That's what we want to hear. >> Exactly. So Daniel, last question to you, this is your first Accelerate? >> It is our first Accelerate. >> Tell us about what excites you about being here? What are some of the things that you've heard and what are you excited about going forward in 2018 with this partnership? >> Yeah, well as we launch our Fortinet connector tomorrow, what really excites me about being here is the huge partner and customer base that Fortinet has built over the last 20 years. Customers and partners that have not only bought the first time, but they're in it to win it with Fortinet. And that's what we are too. I'm excited about the year ahead and enabling people like Basil to be able to sleep on the weekends because they can stitch they're security solutions together in a meaningful way with best of breed technologies and we're honored to be part of that Fortinet Security Fabric for that very reason. >> Well gentleman thank you both so much for taking the time to chat with us today and share your story at Accelerate 2018. >> Thanks a lot. >> Thank you. >> For this cozy panel up here, I'm Lisa Martin my cohost with the Cube is Peter Burris. You're watching us live at Fortinet Accelerate 2018. Stick around we will be right back. (techno music)

>> Announcer: Live from Las Vegas, it's The Cube. Covering AWS re:Invent 2017. Presented by AWS, Intel, and our ecosystem of partners. >> Welcome back to The Cube's continuing coverage of AWS re:Invent 2017. We're live from Las Vegas, day three. There's still a ton of people here. We have a great guest next and we're excited to talk about Mark Kelly, the director of Cloud and Infrastructure Services Architecture, talking with myself, Lisa Martin, and my co-host Stu Miniman. Mark, welcome to The Cube. >> Thank you, glad to be here. >> So Scripps Networks, tell us a little bit about that. I know a few things, HGTV, Food Network, I watch those a lot but, tell us a little bit more about Scripps Network. >> So Scripps, when I say that name, most people do not know it. But we are the leading provider of home, food, and travel content for broadcast, the web, and for emerging technologies at this point in time. So, we've got a lot of brands in our umbrella. You said HGTV, we've got Food Network in there, Cooking Channel, DIY Network, Great American Country, and Travel Channel. >> Yeah so Mark, I know my family binges a lot of these shows so-- >> Really? >> As do I. >> Take us little bit, you know, what's happening in that industry today? You know binging versus watching online, cloud, digital, the joke of course is that at least things are pretty stable and not changing in your world, right? >> Oh I know, absolutely. It is changing on a very rapid pace so, as more and more people, you know, the big buzzword is that cord cutting thing. We have got big concern in out industry around that. But as more and more people talk about it, we're adapting our technologies to be there just to provide for them. So we're on those emerging technologies. We're on all of the said top boxes. Apple TV, Roku, we're actually developing networks specifically for those technologies. So we're trying to adapt. The broadcast world is still our bread and butter, so until we figure out the revenue models for these new technologies, we're gonna be in the broadcast world for some time. >> So Mark, talk us through a little bit of what's your role there. You've been there six years I believe you said. How has Cloud changed the way Scripps works. >> So, Cloud has definitely changed the way we work. When I first came on board, Scripps was looking for a way to move the business faster. Scripps, because it is changing so rapidly, they're in the business of-- They have to compete with startups. And when they're competing with startups, startups don't have the same process as it controls around everything that the bigger enterprises do. So it was an extreme challenge to them. And you know, when they don't understand-- They don't have budget concerns like big enterprises either. When I go in to do a project to launch a new network, they don't understand it. I've got to buy hundreds of thousand dollars worth of hardware or whatever associated with it. So when we're looking at the Cloud, we needed a way to accelerate that. We wanted the make the business process much quicker. We weren't going to have those lead times for purchasing hardware, purchasing software, getting licensed, you know, racking it, stacking it, doing all that. We were looking to ways to move that out of the way so everybody could focus on providing more value for the business. And that was the primary reason we actually started looking at the cloud. >> So from an end user goal perspective, obviously you mentioned speed and having to compete with other networks and other native original content companies. How does the end users demand for 24 by 7 content, how does that drive really, the pace of innovation that Scripps has to meet? >> Yeah that is a very good question. I think we're still trying to figure that out because the whole consumption method for all of our end users is changing. They're going from their traditional TV to where they're watching it on their phone, to switching to their iPads, to everything else in between. So we're developing technology methods so that we're providing it for them as they need it and want it. >> Yeah, Mark can you give us a snapshot of what is your infrastructure look like today? What are the kinds of major things that have changed and what's kind of on the table? >> Today we are running about 3000 instances in AWS. So AWS is our primary cloud provider. We run a lot of auto scaling to take into account our seasonal loads. That's probably one thing that we-- One of the challenges we had going into the cloud is that we were up against certain times of the year when the load was extremely high but it was only extremely high for a couple of weeks. I mean literally we'll be running at two to three hundred percent of what normal was, and it would do it for two to three weeks. So we were having to purchase hardware in advance for that and it would just sit there idle for the rest of the year. So we were looking to ways to avoid that excess purchase and still scale to support our consumers and needs. >> So when you started this journey, and we were talking to Veeam earlier about-- Cloud is a destination, you get there, great, you're done. But when you start this journey you mentioned AWS and we're here at re:Invent, where did you start? What were some of the stakeholders that you had (Mark laughs) on the business side that you had to say, >> Absolutely. >> Alright guys, let's come to the table, we have a great opportunity here, this industry is transforming, as you know, the joke Stu made earlier. Where did you start with those business discussions? >> So I think it was more around trying to figure out that solution. It's like when I said earlier we're trying to get to market quicker. So it was easier to go to the stakeholder and say, "Hey I think I can go this route, and I think I can get you to market twice as fast." >> Lisa: Their ears perk up? >> Oh absolutely. Ears will perk up, eyes will light up. They're like, "Really? How much more is this gonna cost me?" And I'm like, I don't think we're gonna-- They went to represent the on-demand cost models. I think we don't have to do all these big upfront costs and that got a little bit more excitement out of the stakeholders. And we went forward from there. So we would um-- It took a while. Don't get me wrong. Everybody gets set in their ways. They're going to do things normally. Change sometimes is difficult to push through. >> Lisa: Absolute-- Cultural change. Very, very challenging. >> Absolutely. So it is a complete cultural change. They go from your traditional on-premise to the cloud model where you're not managing the hardware as much. You're focusing on the engineering around it. >> So Mark, you bring up managing. You know, monitoring something that's gonna change very differently. Talk a little bit about your people, you know, the skill sets they had to change, what's the difference is kind of before, during, and after that migration. >> So beforehand, when we were on our on premise environment, we were focused on tool sets, for monitoring, and managing infrastructure that were very vast. I mean we had a large number of them. One of our goals moving to the cloud was to consolidate that tool so that we wanted to get down to a minimal set that would actually accomplish our goals to get everything that we needed. So we didn't have to go through training people to learn 50 different tool sets for monitoring whatever it was. Network equipment, storage equipment, or the computer equipment. We were actually focusing on monitoring our applications and still getting some of that underlying infrastructure reporting on our monitoring, but we didn't have to have the same level where we were monitoring the hypervisors, we were monitoring the network switches, that kind of went away. So our focus became more on the operating system up and engineers developed. They no longer had to focus on that hardware. >> So what are you using for that, you know, how many people does it take to do that? Do you know if it can kind of compare to what you had before, yeah. >> Our standard monitoring switch is actually composed of New Relic. New Relic has been a great partner for us. They had the same mindset as us. We're looking to compete with the startups, at the time New Relic was the startup. Because we've been using them for between five and six years now. We brought them in because they had that same hunger and mentality that we were looking for. Their culture mashed really well. And we got in and deployed all of their suites to every environment. We actually leverage it in from development up through production. So we try not to separate our monitoring. We try to keep it all uniform so out troubleshooting gets a lot simpler. We actually have the same people monitoring our DeckStats that are monitoring out production stats. So they can troubleshoot and help get through there. It became a lot easier for them to do that. >> Could you talk about kind of, how many people you had, managing infrastructure before versus the monitoring, and some of the training they had to go through or? >> Before, it was all specialized people. I don't even have that. I couldn't even give you the headcount cause most of that was before my time. >> Stu: Yeah. >> But every individual was actually specialized on each application for monitoring. So it was actually teams that would focus on each part of the business. So as we migrated into cloud, we became more standardized. Made it simpler. We actually have our-- My cloud team is really in between about 15 to 20 people. And we're managing 3000 instances in AWS, and about six petabytes of storage. We've got quite a bit of a content up there already. >> And you're on the customer advisory board for New Relic. >> Mark: I am. >> You mentioned cultural alignment between Scripps and that vendor, and that's really key, but talk to us about this collaboration and it sounds-- Maybe by direction of that, you're able to maybe influence some of the things and help them make their technologies better. >> Absolutely, we've got a really good relationship with them so anytime we have a challenge, one of our current challenges is serverless. As we move, we have a lot of development teams that want to move into serverless. We've been working with the New Relic teams and giving feedback to them on what our challenges are with that, and how we're monitoring it, cause we've got certain things where I wanna be able to monitor those functions in serverless, and I'll need to give a cost back to my stakeholders to say this is what you'd cost. It's challenging to do that now but we're working with the New Relic team to help them deliver some of that knowledge to us. >> Mark, you hit a hot button for me. So bring us inside, you know, why serverless, and what are you hoping to gain from that. I've seen New Relic actually has been tracking for the last couple of years, adoption of containers in serverless so-- >> Containers in serverless is kind of the new hotness you know. We've been moving into serverless primarily because again, it's the next generation of speed for us. Makes it even simpler for the developers to get started, we can give them a standard framework, they can start developing their code and just push deploy and it's running, and they don't have to worry about any infrastructure or managing anything. Again, the challenge has been the monitoring part of it, but working through that and actually getting pretty good results out of it so far. >> So you've got about 70% of your consumer facing side is on AWS which has got some latency sensitive workloads that are still on-prem? >> A hundred percent of my consumer facing properties are on AWS. >> Oh fantastic. >> We do have some workloads that-- Those are really not designed for cloud. It's our end use, end financial systems, our critical business systems that need to be close to those departments. Those actually still live on-premise for us. When we started this journey, the on-premise was-- it was a slow, horrible process but, as we evolved, the cloud, they've evolved that on-premise stuff to keep us with them as well. We're actually looking at, so you know, some of the other monitoring solutions out there. New Relic has been an option for us to actually look at on-premise will, monitoring as well. >> So all the advancements that you guys have achieved in your six year or so transition to cloud that you've talked about. What's next for Scripps? What are some of the maybe new business opportunities that this optimization, cost reduction is enabling? >> So next for us is actually machine learning and AI. We have large initiatives going on that right now. We are trying to analyze our video, analyze our content, lots of it's to help remove some of the manual processes that we have now. Because a lot of that stuff when you're delivering to our different partners, there's certain requirements around the video, and the only way to do it right now is with eyeballs watching the videos. So is this somebody sitting there watching it for hours and hours a day. We're leveraging the machine learning stuff to actually auto classify this video, pull out thumbnails for the authors so they can put it in there, the metadata awareness form, and we're doing lots of things with AI. So we're looking for that to be a really hot feature for us in the next couple of years. >> Excited with what you heard this week from AWS about AI in ML? >> Absolutely. The first day keynotes were completely blown away. They were all things we were looking for. >> Stu: Anything specific that you've been waiting for, or just not waiting for, but got excited by? >> Yeah there was lots of it. The Kinesis video streams were actually really good. The video API, I'm drawing a blank on the exact name of it, but that one actually had some really good features for us because we are looking to do exact things that that one does. We're looking to pull time stamps out for when stuff shows up in videos and provide that back to our end users where they can search and find things in the videos much more quickly. >> Excellent. Well Mark, thanks so much for stopping by The Cube and sharing what you guys have been doing at Scripps Network with us. >> Mark: No problem. >> It sounds like you've seen a massive transition and you're really have a great foundation to continue going forward and >> Thank you. >> Lisa: And we look forward to continue to watch great shows on the network. >> Awesome, thank you guys. >> And for my co-host Stu Miniman, I'm Lisa Martin. We would like to thank Mark for stopping by. You're watching The Cube's continuing coverage from Las Vegas of AWS re:Invent 2017. Stick around guys, we'll be right back.

>> Announcer: Live from the Javits Center in New York City, it's the theCUBE, covering Inforum 2017. Brought to you by Infor. (bright electronic music) >> We are back with theCUBE's coverage of Inforum 2017. I'm your host, Rebecca Knight, along with my cohost, Dave Vellante. We're joined by Barig Siraj and Nasser Bayram. They are both of the Zahid Group, out of Saudi Arabia. Thank you both so much for joining us. >> Good to be here, thank you for having us. >> So I want you to start out by just explaining to our viewers a little bit about what Zahid Group and Zahid Tractor, what you do. >> We are a large group based in Saudi Arabia. We're very diversified. We are mainly in heavy equipment, capital equipment business. We are the importer of Caterpillar machinery and Volvo trucks, Renault trucks, and many other products. More than 40 franchises. We have locations in more than 40 locations, or branches, more than 40 locations for their area, and we have about 4,000 employees, and we mainly focus on providing sales and after-sales services in the kingdom, with a big focus on after-sales. We pride ourselves to be the second to none when it comes down to after-sales services, and we strongly believe in technology and in digital transformation that is sweeping the world of business, and thus far, we embarked on this journey five years ago. >> So what does that digital transformation mean for your business, and generally, and then specifically for IT. Maybe you can start, Nasser. >> Well, first, we have to agree. The business model has changed. There are new business models that has disrupted every single industry landscape out there, and you have to be ready to change and accept that transformation, otherwise, you'll be left behind. The digital transformation takes you beyond managing an organization introducing an IT platform or technology. You have to change the way you think and your readiness to be able to manage where the future is going. If we look, we just attended this session, 52% of Fortune 500 companies in year 2000 no longer exist. They went out of business. In 2015, 55% of Fortune 500 companies lost money. There was no economic crisis or downfall. It simply missed the boat, or they did not, they were not very innovative in their digital strategy or thinking ahead, allowing their industry to be disrupted by people like Uber, Amazon, Alibaba, Souq, an other new entrants with very great innovative ideas and technologies. The old business model of cutting cost or restructuring an organization no longer works. You need to think differently and act differently, and hence, digital transformation becomes critical for your organization, and implementing an ERP platform, standardizing rationalization of your ERP platform, if you have more than one, like in our case, we have more than one, you have to have one standardized platform, one standardized processes, business processes, so that we have one source of data in order to be ready for the future where you can mine that data, have it be by analytics or business intelligence, in order to be able to better serve your customers and learning on about their behavior, about their trends, and how you can better position production services for them in the future to buy, and for you to remain profitable. >> So Barig, okay so now, that's, what Nasser just described, I'm inferring, is much more real-time, much faster, and more data. Your ability to analyze that data wherever it is, how do you, and the processes and people behind that as we talked about technology's the easy part even though some technology's even more complicated than ever. So what does that mean for the IT organization? >> Well for IT organization, we had, and we still have a legacy application built over 30 years. Now, and there we could not reap the benefits of the data mining, the standardization, even that just from AI capabilities on top of that. We cannot reap that until we have that standardized ERP Platform across all our companies. So basically, that's the tall order that was put on our plate, and what we have done, we started the journey. We're partly through it. We went live with two of our companies. We still have three more to go, and we've done it with lesser volume, allowing us to learn and therefore, once we reach our biggest volume company, we would have learned as an organization, not just applying the technology that even the personnel, the change management, the resistant pockets have to deal with all of that. >> Can you give an example of what you've learned along the way, becoming, as we said, it is so much about change management, and it's about getting people over this fear of change. Can you give an example of what you've learned, of what you're doing differently for the companies that have yet to have the rollout? >> The biggest learning experience we had, we just went live with one of our companies, called EJAR, which is a rental company. The success there of the learning, the success is a learning experience. We have a long journey for to go live with five companies, and this is the first one to go live. What we learned by doing that company first is the challenges of change management, how to support on live, challenge of data migration, data cleansing, readiness of the organization, not simply from change management perspective, but also from IT, legal, readiness of your documentation, the contracts, et cetera. It's a vast learning curve to overcome, and we're very happy that we took the strategic decision to go live once more company, so that we gain that experience, and that is the real success we got out of this project now. Now we better we feel we are in better position for the new companies to go forward with, when we go live, we learn so much about change management, where we failed and where we succeeded, we learn better about our readiness, whether it is Zahid Tractor, or Infor, or our IT, our infrastructure, our training program, our after go-live support, the war room was set up to support the go-live, and go in production. We've been two month in production. We're still having some challenges, but nothing that, there are no showstoppers, however, more and more every day, we learn more and more, and we are better positioned to go live with a bing bang on the big company. >> Nasser, as the executive in sort of leading this transformation, do you look for and demand new metrics, new types of KPIs that you want to see? >> Well, definitely, you do the whole thing because of the new metrics. The new metrics have to have built into it, not simply the traditional KPIs of your GPs and revenue and discount and so on, you need to look at customer behavior, customer analytics, pricing positioning, where you are going forward. In the old days, everybody would sit down around the table, say, "Hey, we're number one, okay?" That doesn't hold water anymore. You're number one in what? It's about number one in responding to customer requirements on that customer behavior. Today, with Amazon.com, many retail businesses are challenged, they're going out of business. How do you stop that business model? You can't. So how do you compete? You can. To do that, you have to have the right data in place, the right organization in place, and the right mindset to be able to lead your organization to compete in the new market space. >> Can you give our viewers some examples of the kind of data that you are deriving, in terms of this business analytics, in terms of understanding and deepening your understanding of customer behavior, and what customers want, and how it's changing, how you approach your customers and what you do for them. >> I'll give you a comparison. When we have a legacy systems, what you do at end of day, you extract your data, you transform it and you load it up to your data mart or data warehouse, and then you run your report, and if you're lucky, you have savvy users who can create their own reports on the fly, but with the way we're going with an integrated ERP solution and one standardized platform, we do hope we have the right analytics in place, and business intelligence in place, that we give our management the right data to make decisions, ready to make decisions. Not filtered data, not reports designed, and that takes me straight into your question on IT and ability to IT to deliver. There is no way for any IT organization to cope with the changes. Nowadays, when Amazon went live recently with Whole Food, it took them three to six, three to four months to deal with legal, to deal with retail, with pricing, with the announcement, the whole nine yards of marketing. How did they have their IT ready? That's a challenge. How can you do that in four to six month? That is the challenge in the future. If you don't have the right platform to do that, you will never be able to compete, and data analytics are critical for you to respond or predict the behavior of customer, so before a customer comes next time to the counter, you already have certain statistics that tell you what that person is ready for, and that takes you straight also into IoT. Your products, or our products now, are connected to the Internet. If you don't have IoT in place, connected to your back end, and your analytics, you won't be able to compete, and that would be the differentiator in the future. Those who could do that versus those who will continue to follow the old brick and mortar business model, restructuring and cost-cutting and whatnot. >> So your instrumenting your heavy equipment in the field, presumably, and that's, you're well down the road with that. That changes the data model, it changes the analytics model so I wonder if you could describe that a little bit. I mean, obviously you're processing data at the edge. How much data stays at the edge versus comes back to your central location, maybe you could add some color to that whole equation. >> Well the devices that are put on the machines, there are several ways of putting. The older models, you have, actually the PSSR has to actually go with his laptop, hook it up, suck the data, and bring it back for analytics. The newer models are more, are sending it to, directly to us, and enabling our, what I call tower, to do equipment monitoring, and be able to anticipate, we call up the customer and saying, "By the way." Actually tell the salesmen to call up the customer and saying, "You need to bring your machine in "because it's, you might face a failure "in so amount of time." So improving the customer side, that is, that is that part, but coming back to the organization change issue, we went from a legacy application that the branch managers waited until the end of the month to get the truth, to now being able to, seeing the performance on a daily basis, because they're seeing the truth because everything is connected, whereas before, whatever they did, they don't, their piece of the puzzle, they have a lot missing, and they, information that they waited until it show, send them back there, a report. >> And none of this takes place in the public cloud, is that right? >> No, it does, to add to that, the data is stored in the cloud. Customers have access to it, along with our SOS lab, which is oil sampling lab. They have access to the data to see what is happening, like predictive analysis of their machine performance, and as a result of analyzing the oil, plus any data collected from these machines. We do have cloud implementation. We just went live with our treasury management system. It is on the cloud, and it was our first deployment on the cloud, though the implementation of Infor today is still on-premise. Long-term, down the road, we may be looking at the cloud. >> I got to ask you, we hear Infor messaging about microspecialization, that last mile, all the hard stuff that nobody else wants to do. Is that something that you take advantage of in your industry, or is it? >> I'll give you an example. We utilize the implementation accelerator from Infor for the rental, and it's 77% of our processes map directly into that, so we, that enabled us, that, to have EJAR, which is a rental company, go much smoother. Now, we're working with Infor to enhance their equipment implementation accelerator, and it will be partly the same ratio, around 70% of the processes that we're going to go live with, are the standard processes in the product, out of the box, for the equipment rental, for the equipment business space. >> Our objective is to reduce customization as much as possible, go out of the box, or native, out of the box, as much as possible, but you have to accept the fact, depending on your business environment and some localization requirement, you have to do some customization. However we do have a governance in place, to make sure it's to the minimal. Otherwise, long-term, you'll be challenged with release management and change management and so on, and when you speak of the cloud, if you ever elect to go to the cloud, you can kiss customization goodbye. (Dave laughs) You have to be ready to adopt and adapt. >> And how about your security regime, as a result of the edge and IoT and now, cloud, how is that evolving? >> That's close to my heart. (laughs) >> Yeah, I'll bet, and probably the board's. >> Actually, well, (laughs) actually, interesting enough, many organization, like ourselves included, we invested so much money in building firewalls and security systems to protect what's behind the wall. Now with the cloud, well your most important data is no longer behind the wall. >> Rebecca: It's right there. >> It's outside the wall, so you have to have some kind of a hybrid security system, and you really have to pick the right partner who is hosting your cloud application, leasing your cloud application to you, so the challenge or the perspective of security, cybersecurity, changes drastically and totally, and your understanding of it has to change, otherwise, you just stay behind your own wall and guess what? You can end up locking yourself behind the wall, and you're going to miss the boat, but this does not mean that you'll let down your guard. You have to maintain your security awareness, you have to maintain your security diligence, and you should not underestimate the threats out there, because even if you are on the cloud, the biggest threat nowadays is through phishing. That's what we call the human firewall. Relegating the right awareness, the right education to your organization from within, to understand the threats and the danger of such a threat, otherwise, your password, that's how you access the cloud, you'll end up be compromised and guess what? So will be your data. >> Yes, so, Barig, Nasser, thank you so much for joining us. It's been great to have you on the program. >> Our pleasure. >> Thank you. >> Nasser: Thank you for hosting us, thank you. >> See you guys again, great, thank you. >> I'm Rebecca Knight, for Dave Vellante, we will have more from Inforum after this. 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>> Announcer: Live from the Computer History Museum, in the heart of Silicon Valley, it's theCUBE, covering Food IT, Fork to Farm. Brought to you by Western Digital. >> Welcome back to theCUBE, I'm Lisa Martin, with Jeff Frick, and we have just spent a really interesting educational day at the Fork to Farm event, Food IT. Jeff we've spoken with investors, ag-tech experts, folks in academia who are training the next generation of farmers, to Campbell Soup, who's been around since the late 1800s, are really focused on helping the agriculture and food industry combat the challenges of environmental sustainability, of climate change, of labor shortages, it's been a really, really intriguing day, where tech meets food and agriculture. >> Yeah and just a huge opportunity. One of the themes that kept coming up over and over again, is the average age of the farmers today. Heard 70 something, 60 something, whatever, they're getting old, so there's going to be a huge turnover in this industry, so both a challenge as well as an opportunity for the next generation of ag-people to make some of these changes, and change the way the industry works. The other thing that's really interesting that I found Lisa, is that there's really big social issues that are at play here. We talked about water, we talked about labor, that play into this whole thing, sustainability. And again, tying it back to their theme of its fork to farm, how much of that's now driven by the consumer and the industry, it's kind of a reaction to the consumer, which we see over and over and over in all the other shows that we go. The consumerization IT, driven by younger people's interactions with their phones, is setting an expectation of the way they want everything to work. And so, it sounds like the food industry is really at the cutting edge of this, still really early on, but as we saw in some of those market maps, and the innovation is rich, feels like we're really at the start of this thing. So even though this show has been around for a few years, they have the big show in Salinas next week, the Forbes show, that's still really early days of leveraging tech, innovation, to change the food industry. >> It is, and you brought up that the labor shortages, and that was echoed quite a bit today, for a number of reasons. One, the aging population of farmers as you mentioned. Two, also in California, the minimum wage going up, and that's not only going to be a problem Jeff for farmers, but it's actually now pervading into the retail space, where they're going to have to start depending on robotics to be able to create, or to reduce their cost, to provide even fast food. That was something that was quite interesting to me, I hadn't really quite thought about, from that channel perspective. >> Right, right. >> And then as you mentioned, on the tech enabled consumer side, I was talking with Jeff earlier, I kept thinking farm to fork, 'cause farm to table is so trendy now, right? There's a lot of apps. And you gave me this a-ha grasshopper look, and it was really because as consumers we've really demanded so much. We want transparency, we want to know exactly what's in things, and we want organic, and hormone-free, and we also want things delivered whenever, and wherever we want them. We think of the distribution model, has really become very decentralized, and a lot of that being driven by the consumer. On the farm side too, regarding the attrition, there's also a lot of antiquated, especially in the post-harvest supply chain, things that are still written down on paper, traceability is a huge challenge for them. And I think from some of the things we heard today, a lot of the farming, especially in California, they can't really quite see all the data that they have, but they are sitting on a lot of information, that not only could make their farms more efficient, but could also facilitate you think, even knowledge transfer to the next generation of farmers. Right, right. Yeah a lot of talk about kind of there wasn't a lot of data, now it's a data flood. So how do you use those data sources to be more intelligent in what you do? And I specifically asked some of the guests, you know, are kind of the classic big data players participating in this space, and she said, "Not really." They're all kind of holding off on the side waiting to get in. But these are big numbers, this is a big impact. The professor from St. Louis Episcopal talked about a billion dollars worth of strawberries that you got to get off the field, and if you don't have the labor to get it off, and the data to get the labor and to time it right, it's a billion dollars worth of strawberries, and these are big numbers. And the other thing that just fascinated me, is again, this power of the consumer. The Google guy who took basically what was a service just to feed employees and keep them around so they write more code, but using that as a platform to drive much more thoughtfulness and intelligence. And supply chain changes around food, and even called it food shot in reference to the moon shot. >> The moon shot, yes. >> Enabled better diets, shift diets, food transparency, reduced loss and waste, accelerate transformation to a circular food economy. So, and they said, I think he's been at it for 15 years or thereabout. So really an interesting kind of a twist, on what you would not expect from the food service people, you think of them just supplying food. >> Exactly. >> Not trying to drive cultural change. >> Exactly, and trying to scale, but they're using data from their own googlers, to help determine and evaluate what people are doing, what they want, preferences, making it more personal, and using data in that way to also then facilitate some of the upstream, you know from the supply perspective, making things, meeting those challenges that the consumers are demanding, but you said he's been at Google for five years, and when he first got the call being in hospitality for so long, he just thought, "Google, what do they want to talk to me for?" And how revolutionary they've been, and you can think of how much education can happen from Google Food alone. I was quite blown away by that. >> Yeah, the other kind of theme is unused resources. So, one of the food trucks that they had seaweed. Why seaweed? Because it takes no fresh water, it takes no fertilizer, and it's carbon negative. So not really about how does it taste, but some specific reasons to try to make seaweed a better food, a more satisfying food. Talked about kale, and really again what a great example of a, can't say it, Fork to Farm tradition, 'cause before kale was a throwaway, nobody grew kale, now suddenly everybody wants kale smoothies, and so there's nothing, plant became something of importance, driven by the consumer, not necessarily by the producers. So, very dynamic times. I think again, the trend we see over and over and over, finding the hollowing out of the middle. You know, you don't want to be just a generic provider in the middle, you better have massive scale, or you better be a real specialty provider. And then finally the ramifications of the Amazon purchase of Whole Foods, really validating, yes you want digital, yes you want data, yes you want to provide better customer service. But at the same time, you still need a physical presence, kind of validating the physical presence of the store like Whole Foods. So really a very dynamic activity going on in this space. >> And it'll be interesting to see what happens over the next five to 10 years, as farming generationally changes hands. And there is technology that's available today, right? We talked about big data, there's many, many sources of public data, whether it's satellite imagery, water data that can be utilized and then paired with private data that a farm has. Or using GPS devices on tractors and combines, robotics. You talked to the inventor of the Sally Salad machine, there's a lot of technology that might be, I don't know if I'd say ahead of its time, but I think from a farming perspective, there's a little bit of a gap there right now. So it'll be very interesting to see how farms evolve from a technology perspective. I love how the Forbes AgTech Summit, I think it's tomorrow and Thursday in Salinas Valley, what a great juxtaposition of Silicon Valley and a world hub of technology innovation, to Salinas, which is the salad bowl of the world. I think that is quite interesting, and some of the dynamics that they've seen, I think this was their fourth event tomorrow. >> Jeff: Fourth event, right, right. >> Really starting to get more farmers interested in understanding the potential that ag-tech can have on profitability, efficiencies, reducing waste, even things like discovering and preventing foodborne pathogens. >> Right, and robots, we need robots, we don't have enough labor. Michael Rose said there's going to be a shortage of hundreds of thousands of line cooks. Just regular, ordinary line cooks at restaurants, and that's really kind of one of the applications of the salad machine, because as you hit the button below that cook, you can hit the button to load that salad, while you run off and pull the rest of the entree meals together. So, again, it's really fun to see the consistent themes that we see over and over, that's computing cloud and data-driven decision making, applied to what's arguably one of the most important things going on, which is feeding us a lot of conversation about the world's population getting to 10 billion in the not too distant future, that have to be fed. And again, with the aging of the population, the traditional farmers, a real opportunity to do kind of a refresh with a bunch of people that have grown up with these things. So, really cool show, a great day, hope you had fun, I had fun. >> Oh, I had a great time, it was really educational. I think that you hit the nail on the head, there's a tremendous amount of opportunity. I think what the Mixing Bowl is doing, along with Better Foods, is really bringing the people that are creating food, and producing it together, and connecting them with the people that are creating technology. So, I think this is the tip of the iceberg head of lettuce, maybe? So, I am excited to see what happens over time, but not only was it a great event, but I'm now very hungry. >> Now you're very hungry, there's more food trucks outside. Alright Lisa, well thank you again for hosting. >> Thank you. >> Again, another great show. I think last time we were together was at the NAB. >> NAB. >> Talking about media entertainment, so the digitization, transformation continues, driven by all these huge macro-factors of cloud, big data, so the beat rolls on. >> It does. >> Alright, she's Lisa Martin, and I'm Jeff Frick, you're watching theCube. Thanks for watching, we've got a busy spring coming to an end. Had a little bit of a lull in the summer then we'll hit it hard again in the fall, so thanks for watching siliconangle.tv, youtube.com/siliconangle, and siliconangle.com for complete coverage of a lot of stories beyond just theCUBE. I'm Jeff Frick, signing off with Lisa Martin from Food IT, from Fork to Food, thanks for watching.

>> Announcer: From the computer history museum, in the heart of Silicon Valley, it's The Cube. Covering food IT, Fork to Farm. Brought to you by Western Digital. >> Hey, welcome back to The Cube. From the food IT event, From Fork to Farm, yep, you heard that right, Fork to Farm. I'm Lisa Martin. Really excited to be joined by my next guests who are influencing the food chain with Big Data, Cloud, IoT and Blockchain in some very, very interesting ways. We have Rob O'Reilly, senior member and technical staff of Analog Devices. Welcome. >> Thank you. >> And we have Raja Ramachandran, the founder and CEO of Ripe.io. Welcome. >> Thank you Lisa. >> So I made that joke about the Fork to Farm because we think so often how trendy it is, farm to table, farm to mouth. And this has been a really interesting event for us to talk with so many different people and companies across the food chain that we often, I think, take for granted. So Rob, wanted to kind of start with you. Analog Devices has been around for 50 years. You serve a lot of markets. So how is, and maybe kind of tell me sort of the genesis, and I know you were involved in this, of Analog Devices evolving to start using Cloud, Big Data, IoT in the food and agriculture space. What was the opportunity that you saw light bulb moment? >> Yup. It's an interesting story. We started with a piece of technology, a sensor that we can connect. I was looking of an app to apply, 'cause it was a full sensor to the Cloud strategy I was working on. And through some conference attendees that I had met and from a fellow who's now our partner, we kind of put together a strategy of "Well we've got the sensor to the Cloud, "where would we apply this?" And we decided though a little bit of banter, tomatoes. And most of it was because, in New England specifically, there's a lot of, there's 7,000 farms in Massachusetts. >> Lisa: Wow. >> Not all of them produce tomatoes, but a lot of them do. So it was like having a test bed right in our backyard. And from that point it's grown to what it is now. >> And I hear that you don't like tomatoes. >> I really don't like tomatoes. >> Lisa: What about heirloom tomatoes? >> I don't like any tomatoes. >> Lisa: Mozzarella, little basil, no? >> No, no. (laughs) I don't mind pasta sauce so much, but that's just because it's all salt. >> Lisa: That's true. >> And sugar. But no, and I've managed to get through this entire project without anybody forcing me to eat a tomato, so. >> That's good, they're respectful. >> I'm proud of that. >> So I was joking earlier, we cover a lot of events across enterprise innovation, and we were at a Hadoop Dataworks events a couple weeks ago and one of the guests was talking about Big Data and how it's influencing shipping, and how shipping companies are leveraging Big Data to determine how often they should clean the ships to remove barnacles 'cause it slows them down. So the funny thing that popped into my mind from that show is, barnacles and Big Data? Never thought that. Today, the wow factor for me, the internet of tomatoes. What is the internet of tomatoes? >> The problem statement when we started was "Why do tomatoes taste like cardboard?" >> Lisa: He really doesn't like tomatoes! (laughs) >> And, you know, in order to go dig into that was let's collect data. So there's a variety of methods that we use to collect the data. We had to create all of this on our own, so we created our own apps for the phones, our own matchups for the web, our own gateways. We built our hardware, we 3-D printed all the housings, and two of us just went off and started to deploy so we could collect data. The second half of it was, "well, what is in the tomato? "and why does it taste the way it does?" So we started doing some chemistry analysis. So a bunch of refractometers and other instruments so we can see what the sugar levels were, what the acid levels were. We infused ourselves into the Boston Tomato Contest, which they have annually. So we showed up, we looked like the Rolling Stones. We showed up with cases of, trap cases of equipment. It took us about 11 and a half hours to test 113, I think it was, tomatoes, and then we compared those to the chefs' scorecards. And in the chef's scorecard, there wasn't just a taste profile, there was the looks and everything else. Well I found a few markers between what the chef's profile said was a good tasting tomato and what the chemistry said. So a year later we showed up with our optical solution and we managed to test 450 tomatoes. >> Wow. >> About 100 of those go to the slicing table, so we had information on 100 of them and we did the same thing. So it got to the point to where we at least had that reconciliation of "what's the farmer doing "and how does it taste?" And by bringing Raja and his group in, we're bringing a lot more of other Big Data, if you will. Other weather data, aerial drone data, you know, anything we could find in a telematic range that would affect the processing or whatever of the tomato. So that in a nutshell is the internet of tomatoes. >> And is this something that, you know, being able to aggregate Big Data from a variety of sources, something that you're planning to then take to, I heard you earlier in the talk, talking about kind of at the relationship building stage. Is this a dialogue that you're having yet with farms? You mentioned 7,000 farms in Massachusets. What's that kind of conversation like? >> Well that's a very interesting dynamic and I think, you know, that data point for the industry is you better go talk to the farmer. It's really been interesting, the hesitation from a farmer to talk to a semiconductor company was odd. But I wasn't John Deer, I wasn't Monsanto, so they were a little more open. And they understand, a lot of these farmers that I'm dealing with now are generational, you know they're fifth, sixth generation. They really haven't made significant change on their farm in 100 years. >> Probably nor do they have a lot of data that's automated, right? There's probably a lot of things that are in Excel. >> And a lot of it is, I mean beyond their first level of contact, say with a seed or a pesticide manufacturer, They have no idea what's going on in the rest of the world. Unlike, you know, a lot of the big, large farms that we see. But at the smaller region, they're regional. And we've still have Hatfield-McCoy type things going on in New England, where families don't talk to each other, they don't share information. So through one of our work groups, we actually invited two of them, and I felt like match maker. We were trying to just get these two to talk. And they did, and they both realized that they were spending way too much money on fertilizer, and they were both over watering. So, it's still Hatfield and McCoys but at least I think they wink at each other every once in a while. >> Right, I love that you bought that up. That was something that was talked about a number of times today is the lack of collaboration maybe that's still in the sort of competitive stage. So Raja, talk to us about Ripe.io. First of all, I think the name is fantastic, but Blockchain and food. What's the synergy? And what opportunity did you see coming from the financial services industry? >> So, you know one of the key points about what we felt brings all this together is creating a web of trust. And so in financial markets, insurance markets, healthcare markets, you know big institutional regulated markets, there's a lot of regulations that really bind together that notion of trust, because you have a way in which you could effectively call out foul. Now, so there's a center of gravity in each of those industries, whether it's a central bank, you know or a state regulator insurance, so the government in healthcare. Here, there's not. It's disparate. It's completely fragmented, yet somehow magically we all get food everyday, ane we're not dead you know. So from that perspective we just marvel at the fact that you're there. So, bringing Blockchain was a way to basically talk to the farmer, talk to the distributor, talk to the buyer, the producer, and all these different constituents, including certifiers, USDA, whomever it might be. And then also even health to health companies, right, so that you can relate it. So the idea is to basically take all of these desperate sets of data, because they don't necessarily collaborate in full, capture it in the way that we're working with ADI so that you can create a real story about where that food came from, how is it curated, how did it get transported, what's in it, you know, do I get it on time, is it ripe, is it tasty and so on, right? And so we looked at Blockchain as a technology, an enabling technology that quickly captures the data, allows each to preserve its own security about it, and then combine it so that you can achieve real outcomes. So you can automate things like, were you sustainable? Were you of quality? Did you meet these taste factors? Was it certified? That's what excited us. We though, this is a perfect place because you've got to feed 9,000,000,000 people and no one trusts their food, you know? >> Lisa: Right. >> So we felt this would be an excellent opportunity to deploy Blockchain. >> And it's interesting that you know, the transparency is one of the things that we hear from the consumers, you know. We want all these things. We want hormone free, cage free, et cetera. We want organic, we want to make sure it is organic, but we also want that transparency. I'm curious since you are talking to the farmers, the distributors and the consumers, what were some of the different requirements coming from each, and how do you blend that to really have that visibility or that traceability from seed to consumption? >> And it's a good point right, because there's all these competing factors where farmers want certain information done, they don't want the price to go to zero because it's so commoditized. The distributor, not entirely sure if they want anybody to know what they do is if they deliver it, they've done their job. The aggregator, a grocery store, a restaurant or whomever, are really feeling the pinch of demographic changes. Not only in America, but globally, you know about this notion that "I need to know more about my food". Millennials are doing it, look at Amazon and Whole Foods. >> Lisa: Yup. >> That is a tipping point of like where this is all going to go. So for us, what Blockchain does allows for each of those drivers to remain clean. And so in essence, what you can do is you take something called smart contracts, not a great word but basically these are codes in which you've got a checklist or if-then statements that you can say, "What does the farmer want?" "What is the distributor doing to get something there?" And of course the buyer. And so in that sense, we've talked a lot about a scorecard or this notion that you can basically highlight and show all of these different values, so that if the consumer is looking for, you know, I definitely want this in my lettuce, in my beets, in whatever it is, and I need to make this type of salad, how acidic should my tomatoes be? Well that's hard to count, like combine all that information. Since we're capturing that data set and validating it to make sure that they're true, then you actually enable that trust for that consumer. So the consumer may want a lot of information, the issue is will they pay for it? There's some evidence that they will. The second part is, you know, does the grocer have the ability to manage wide varietals in their shelf space, and so on. All the techniques that a grocer would go through, yet they want a clean supply chain. >> Lisa: Right. >> So you know, so like what're we're saying is that this is definitely not easy. And so we're taking it where the influencer of the entire chain is able to help drive it, in the meanwhile we're trying to help create a farmer community that creates a level of trust. Bind those together, we believe Blockchain and a lot of the technology that ADI is deploying helps achieve that. >> And it sounds like from a technology perspective, you're leveraging Blockchain, Big Data, aggregating that to help farmers, even consumers, grocers, retailers, become more data-driven businesses. >> Oh absolutely. I mean in one instance we've got, you know a customer that they're learning how Blockchain can be used to open up their markets and improve their existing customer service. So what they have are like data sets, you know Rob would definitely understand this, but basically you have data set on like what's best for apples, pears, avocados to ripen, you know. Now, they know it in their heads, right? But the issue is, they don't know when there's conditions that change. The grocery store says I want Braeburn apples to be 20% more crisper, well they actually have the answer but they don't know how to tie all that together. >> Lisa: Right. >> So this data-driven capability exposes automation, so that you can fulfill on that. Create new markets, 'cause if your growers don't have it you can go find it from elsewhere. And for the consumer, you're going to deliver that component on time. And so in that sense, you know these things are revealed as ways to, not only like lower cost you know, because in the end Blockchain has this sort of notion that it lowers costs. Like any technology, if you insert it, it typically adds costs. And I'm not saying that our Blockchain does, but the greater value is branding, preserving it, you know. A better economic consequence about it, a better customer satisfaction because I now have knowledge in transparency. >> Lisa: Right. >> So you can't value these things right, because I'm a millennial like all of a sudden I got all my information, well how did you value it? I just paid $60 at Whole Foods, or is it something else? >> Lisa: Right. >> So we think that there's whole new economic revitalization about the entire farming system and the food nag system, because if you show the transparency, you've got something. >> That's so interesting. Last question, and we're almost out of time, Rob you mentioned a lot of small farms in Massachusets. Where are those small farms in terms of readiness to look at technologies and the influence of Big Data? Is it still fairly early in those discussions, or is your market more the larger farms that ... >> I said it earlier, we're at the beginning of the beginning. I was actually shocked, excuse me, when I went out and started talking to them. I was under some assumption that a lot of this was already going on. And it turns out it's not, certainly at that level. So we were like new to these guys, and the fact that we had a technology that would help them was unique to them. The issue was, well how do you communicate with them? How would you sell that? What's the distribution channel? So through a lot of the workshops that we do with the farmers we ask the question, "If their is new technology and you want to go get it, "what do you do?" They google it. I said, "Okay, that's probably not the answer "I was looking for." (laughs) But no, the supporting infrastructure, the rest of the ecosystem they need to take advantage just isn't there yet. So a lot of that I think is slow for the adoption, but it's also kind of helped us because we're working on technologies. You know, timing is everything. So the fact that we've had time to catch up to what we thought was really needed, and then learned more from the farmer, well no, no this is really what they want. So we've been able to iterate. You know, we're a very small team. We've been able to fail miserably many, many times. But the good news is, when we're successful that's all people see. And the farmers are starting to see that, that hey, we're getting actionable data. You're telling me things that I kind of knew, 'cause they fly by the seat of their pants a lot. >> They want it validated, verified. >> Oh yeah, they're very frugal. >> Trustworthy, as you said Raja. >> There's a big push back to spend any money on anything at a farm. That's just the way it is, it's not anything unique. So when you show up now with some technology that could help them, they just want to make sure that you're spot on, you can predict what it is, and when they hand me the money they can start planning on the return on their investment. >> Well gentlemen, we want to thank you so much for sharing your insights, Blockchain of food, what ADI is doing in their 50th year. Sounds like the beginning is very exciting and we wish you the best of luck. I'm not going to hold my breath that you're going to like tomatoes but, you know. (laughs) We wish you the best of luck and enjoy the rest of today. We want to thank you for watching The Cube at the Food IT event, From Fork to Farm. I'm Lisa Martin, thanks for watching. (upbeat pop music)